Gaussian Graphical Model
John Zhuang
2024-06-20
library(rags2ridges)
library(ggplot2)0. Data Preparation
data.horizontal = readRDS("data/derived/data_horizontal_joined.rds")
data.numeric = as.matrix(data.horizontal[, 1:701])
data.Y = data.horizontal$vital_statusgroup.mRSG = 1:470
group.R2Gn = 471:668
group.RPPA = 669:7011. Wholistic analysis by omic
A. RPPAArray
data.RPPA = data.numeric[, group.RPPA]LambdaMax is reduced from 1000 to 10 as all optLambda values are < 10.
Unused 3, 5 and 10-fold CV are disabled and presented in document 03b.
set.seed(42)
opt.RPPA = optPenalty.kCVauto(Y = data.RPPA, lambdaMin = 1e-11, lambdaMax = 10)
#opt.RPPA.10 = optPenalty.kCVauto(Y = data.RPPA, lambdaMin = 1e-11, lambdaMax = 1000, fold = 10)
#opt.RPPA.5 = optPenalty.kCVauto(Y = data.RPPA, lambdaMin = 1e-11, lambdaMax = 1000, fold = 5)
#opt.RPPA.3 = optPenalty.kCVauto(Y = data.RPPA, lambdaMin = 1e-11, lambdaMax = 1000, fold = 3)
#setNames(c(3,5,10,43), c(opt.RPPA.3$optLambda, opt.RPPA.5$optLambda, opt.RPPA.10$optLambda, opt.RPPA$optLambda))
opt.RPPA$optLambda## [1] 0.0005458799edgeHeat(opt.RPPA$optPrec, diag = F, textsize = 7)## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
#P = ridgeP(covML(data.RPPA), lambda = 0.001)
#test = covML(data.RPPA)
CNplot(covML(data.RPPA),
lambdaMin = 1e-11,
lambdaMax = 1000,
step = 5000,
Iaids = T,
vertical = T,
value = opt.RPPA$optLambda)## Perform input checks...
## Calculating spectral condition numbers...
## Calculating interpretational aids...
## Plotting...
#heatmaps.RPPA = vector(length = 9)
#for (i in (seq(0.1, 0.9, 0.1))) {
# heatmaps.RPPA[10*i] =
# edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
# diag = F, textsize = 7,
# main = paste("False Discovery Rate cutoff:", i))
#}
## I have to do the below, otherwise edgeHeat maps do not show up.
i = 0.1
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 7,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 235
## - Corresponding to 44.51 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.2
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 7,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 231
## - Corresponding to 43.75 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.3
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 7,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 143
## - Corresponding to 27.08 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.4
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 7,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 143
## - Corresponding to 27.08 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.5
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 7,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 120
## - Corresponding to 22.73 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.6
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 7,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 74
## - Corresponding to 14.02 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.7
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 7,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 49
## - Corresponding to 9.28 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.8
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 7,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 27
## - Corresponding to 5.11 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.9
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 7,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 26
## - Corresponding to 4.92 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
#P0 = sparsify(P, threshold = "localFDR")
P0.RPPA = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=0.9)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 26
## - Corresponding to 4.92 % of possible edges
## #edgeHeat(P0.RPPA$sparseParCor, diag = F, textsize = 7)#dev.new(width = 10, height = 10, unit = "in", noRstudioGD = F)
set.seed(42)
Ugraph(P0.RPPA$sparseParCor, type = "fancy", Vsize = 15, Vcex = .5,
cut = 0.5,
lay = "layout_with_fr", prune = T,
main = "RPPA Array data\nFDRcutoff at 0.9, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] -1.5479108 0.7458901
## [2,] 0.4028637 0.7202271
## [3,] -1.5105063 2.8475053
## [4,] 0.5670638 2.3929733
## [5,] 1.1082156 0.1850464
## [6,] 1.1386108 1.4869290
## [7,] -0.2561579 1.2932066
## [8,] 0.2952780 -0.7508837
## [9,] 1.6798781 2.5247950
## [10,] -0.4124558 0.1002414
## [11,] -1.9197119 -1.8282442
## [12,] 0.2057263 0.1630035
## [13,] -0.4324510 1.8766875
## [14,] -2.6393477 -1.1574446#Ugraph(P0.RPPA$sparseParCor, type = "fancy", Vsize = 15, Vcex = .5,
# cut = 0.5,
# lay = "layout_in_circle", prune = T,
# main = "RPPA Array data\nFDRcutoff at 0.9, Strong Edge cutoff at 0.5")GGM.RPPA = as.data.frame(GGMnetworkStats(P0.RPPA$sparseParCor, as.table = T))## Warning in log(det(S[-j, -j] - S[-j, j, drop = FALSE] %*% S[j, -j, drop =
## FALSE]/S[j, : NaNs producedGGM.RPPA.order = GGM.RPPA[order(GGM.RPPA$degree, decreasing = T), ]
GGM.RPPA.order[1:10,]## degree betweenness closeness eigenCentrality nNeg nPos mutualInfo
## IGFBP2.RPPA 7 8.850000 0.06666667 1.0000000 7 0 -0.99348219
## PDCD4.RPPA 6 9.400000 0.06250000 0.8081473 6 0 -0.41366528
## GAPDH.RPPA 6 5.200000 0.05555556 0.8364804 6 0 -1.17208844
## FASN.RPPA 5 7.283333 0.05555556 0.6505305 5 0 -0.68789464
## MYH11.RPPA 5 3.200000 0.05555556 0.7522806 5 0 -0.81135162
## TFRC.RPPA 5 12.400000 0.05882353 0.6487760 5 0 0.35208392
## GATA3.RPPA 4 4.250000 0.05263158 0.4783506 4 0 NaN
## ATM.RPPA 4 1.416667 0.05263158 0.6626447 4 0 -0.08079784
## TTF1.RPPA 3 0.000000 0.04545455 0.5295691 3 0 -0.77092600
## G6PD.RPPA 2 0.000000 0.04545455 0.3364328 2 0 0.53890169
## variance partialVar
## IGFBP2.RPPA 0.3702850 1
## PDCD4.RPPA 0.6612222 1
## GAPDH.RPPA 0.3097194 1
## FASN.RPPA 0.5026332 1
## MYH11.RPPA 0.4442572 1
## TFRC.RPPA 1.4220279 1
## GATA3.RPPA -0.4200167 1
## ATM.RPPA 0.9223801 1
## TTF1.RPPA 0.4625845 1
## G6PD.RPPA 1.7141232 1ggplot(GGM.RPPA.order, aes(x = reorder(rownames(GGM.RPPA.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.RPPA.order$degree), linetype = "dashed", color = "red") +
geom_hline(yintercept = 1, linetype = "dashed", color = "blue") +
scale_x_discrete(name = "RPPA Array", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-1")
B. RNASeq2GeneNorm
data.R2Gn = data.numeric[, group.R2Gn]set.seed(42)
opt.R2Gn = optPenalty.kCVauto(Y = data.R2Gn, lambdaMin = 1e-11, lambdaMax = 10)
#opt.R2Gn.10 = optPenalty.kCVauto(Y = data.R2Gn, lambdaMin = 1e-11, lambdaMax = 1000, fold = 10)
#opt.R2Gn.5 = optPenalty.kCVauto(Y = data.R2Gn, lambdaMin = 1e-11, lambdaMax = 1000, fold = 5)
#opt.R2Gn.3 = optPenalty.kCVauto(Y = data.R2Gn, lambdaMin = 1e-11, lambdaMax = 1000, fold = 3)
#setNames(c(3,5,10,43), c(opt.R2Gn.3$optLambda, opt.R2Gn.5$optLambda, opt.R2Gn.10$optLambda, opt.R2Gn$optLambda))
opt.R2Gn$optLambda## [1] 0.2326298edgeHeat(opt.R2Gn$optPrec, diag = F, textsize = 1)## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
#P.R2Gn = ridgeP(covML(data.R2Gn), lambda = opt.R2Gn$optLambda)
CNplot(covML(data.R2Gn),
lambdaMin = 1e-11,
lambdaMax = 1000,
step = 5000,
Iaids = T,
vertical = T,
value = opt.R2Gn$optLambda)## Perform input checks...
## Calculating spectral condition numbers...
## Calculating interpretational aids...
## Plotting...
i = 0.1
edgeHeat(M = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 3287
## - Corresponding to 16.85 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.5
edgeHeat(M = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 3285
## - Corresponding to 16.84 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.7
edgeHeat(M = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 3131
## - Corresponding to 16.05 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.9
edgeHeat(M = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 2511
## - Corresponding to 12.87 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.999
edgeHeat(M = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 1540
## - Corresponding to 7.9 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.999999
edgeHeat(M = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 1057
## - Corresponding to 5.42 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 1-1e-10
edgeHeat(M = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 705
## - Corresponding to 3.61 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
Smallest possible FDRcut:
#P0.R2Gn = sparsify(P.R2Gn, threshold = "localFDR")
P0.R2Gn.min = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=1-1e-14)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 603
## - Corresponding to 3.09 % of possible edges
## #dev.new(width = 20, height = 20, unit = "in", noRstudioGD = F)
set.seed(42)
Ugraph(P0.R2Gn.min$sparseParCor, type = "fancy", lay = "layout_with_fr",
Vsize = 5, Vcex = .45, prune = T, cut = 0.5,
main = "RNASeq2GeneNorm data\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] 1.734183237 -4.52514658
## [2,] 0.187754883 -5.79499701
## [3,] 2.641173238 0.55322908
## [4,] -2.638602483 -0.77703143
## [5,] 1.150737294 -2.89606430
## [6,] -0.836133566 -3.52230963
## [7,] -0.044278251 -2.31349045
## [8,] 0.007581762 1.83571803
## [9,] 1.338881693 1.23107667
## [10,] -0.437822039 4.16731768
## [11,] 0.784602010 -3.37550872
## [12,] -2.187820344 -6.99131445
## [13,] 0.379668303 -0.34942823
## [14,] -1.434690173 -5.63822022
## [15,] -1.285245939 0.82321549
## [16,] 2.430335007 -0.52389937
## [17,] -0.281420424 -7.64483828
## [18,] 0.882799717 0.59513840
## [19,] 0.717046115 -2.95308084
## [20,] -3.546965174 -1.61422108
## [21,] -2.827530468 -6.03414478
## [22,] 7.361957745 -5.31812681
## [23,] 6.396660888 -6.28068886
## [24,] -4.271808899 1.81250327
## [25,] 0.662383245 -1.62350226
## [26,] -4.428564821 -5.48161601
## [27,] 1.042613104 -3.25287706
## [28,] 2.813350211 -5.39106920
## [29,] 7.887556538 -3.11103814
## [30,] 2.019986964 -1.66200788
## [31,] 3.836535334 4.01155736
## [32,] 3.296264316 -4.63849118
## [33,] 2.100404963 2.92161147
## [34,] -3.931202822 -4.65406074
## [35,] -2.061058751 3.54446818
## [36,] 6.223492357 0.88253785
## [37,] 1.626868902 -1.70237272
## [38,] 2.757949685 2.45150248
## [39,] 7.886403603 -3.75557154
## [40,] 0.307707168 -4.30409695
## [41,] 3.757015505 -0.78846722
## [42,] 0.230256254 2.81021062
## [43,] 1.514424252 -2.97663659
## [44,] 6.144805883 -5.59316554
## [45,] 3.598793821 1.17084830
## [46,] 1.637040355 -5.49258987
## [47,] 0.475504485 -2.40134990
## [48,] 0.879764700 -0.64488324
## [49,] -0.059042848 -1.89897013
## [50,] 2.302391541 -0.93921306
## [51,] -1.273647691 -1.69132040
## [52,] -2.780583087 1.31318678
## [53,] 1.007030082 2.25470119
## [54,] 7.195825733 -4.69806671
## [55,] 7.970541337 -2.44420367
## [56,] 1.167168105 -2.32455851
## [57,] 2.323238408 -5.79527211
## [58,] 3.648914570 -1.61267241
## [59,] 0.759271798 -0.75049077
## [60,] 7.004036835 -4.00013633
## [61,] 1.591133837 -4.99689321
## [62,] 7.399422974 -1.21123110
## [63,] 0.230396323 -1.78361547
## [64,] -1.050182519 -0.01760538
## [65,] 1.586131551 -2.49346435
## [66,] 2.122102160 -4.06924767
## [67,] -0.027644040 -0.86106744
## [68,] -2.088749183 -1.36182464
## [69,] 1.810561409 2.08040987
## [70,] -4.001628743 -0.98762795
## [71,] -2.365557261 1.78401619
## [72,] 0.536228884 -7.82545926
## [73,] 1.231075008 3.99261398
## [74,] 2.013259015 0.89696820
## [75,] 1.803400427 0.05710434
## [76,] 7.766288803 -4.41329093
## [77,] 1.294726490 -1.02235807
## [78,] 3.672599424 -2.06899349
## [79,] -1.357178588 0.23350687
## [80,] -0.141142539 0.27595889
## [81,] 7.265260849 -2.42708854
## [82,] 5.808584489 -1.64755706
## [83,] 5.206278766 -0.03164990
## [84,] 3.339758296 3.04458630
## [85,] 1.453872978 -1.89868191
## [86,] 3.326411172 -3.52533521
## [87,] -1.281319836 3.93889721
## [88,] -2.764189904 -3.57038591
## [89,] -0.638028853 -2.95990608
## [90,] 2.437551395 0.17761034
## [91,] 2.534466055 -1.53685749
## [92,] 6.585230855 -4.96725732
## [93,] 2.467053564 -3.37815746
## [94,] -1.949140944 -4.41216124
## [95,] 2.694710102 -2.06111246
## [96,] 3.500739370 -2.90935166
## [97,] -2.482087355 -0.29333847
## [98,] -2.844788490 -2.22001124
## [99,] -4.718982067 1.05583422
## [100,] 1.261098219 -0.21186879
## [101,] 1.018176674 -1.58539686
## [102,] -2.182249244 0.16057209
## [103,] -1.382605999 -2.59402426
## [104,] 3.843736133 -5.54822322
## [105,] 2.225763425 -2.61730878
## [106,] 3.059246603 -0.36797989
## [107,] 0.976699024 -4.06922045
## [108,] -1.457858265 -7.36953684
## [109,] 3.052371551 -1.18837643
## [110,] 0.264604338 -2.17261603
## [111,] -1.331484062 -3.50807436
## [112,] 1.489003947 -2.20771029
## [113,] -4.362095387 -3.83248720
## [114,] 1.347674848 -6.89846940
## [115,] 1.797147290 -0.75677614
## [116,] 0.834823677 -1.30727962
## [117,] 0.863047661 -1.95897865
## [118,] 6.867964238 -5.81772967
## [119,] -0.329895281 2.36299975
## [120,] 0.274766264 -1.24340464
## [121,] -0.672761136 -0.90346316
## [122,] 1.579031736 -1.27514605
## [123,] 0.879755903 -2.54512674
## [124,] 0.225210882 -3.24792770
## [125,] 1.979088436 -2.78444112
## [126,] -0.486851981 -3.86978951
## [127,] 7.647255583 -1.79764059
## [128,] -3.372571624 -5.44181712
## [129,] -1.574602283 -2.10270063
## [130,] 5.326451729 -6.79766945
## [131,] 0.177443641 0.64687601
## [132,] -2.283346517 -2.19758016
## [133,] -0.422031066 -0.49980201
## [134,] -5.016746983 0.23158176
## [135,] -1.859789180 2.11792595
## [136,] 3.324498316 -6.04928835
## [137,] 7.191857522 -3.27611071
## [138,] -2.464094482 -2.83020052
## [139,] 0.031594375 -3.00722305
## [140,] 0.043516612 -2.62598756
## [141,] 5.815344796 -6.43640156#Ugraph(P0.R2Gn.min$sparseParCor, type = "fancy", lay = "layout_in_circle",
# Vsize = 5, Vcex = .5, prune = T, cut = 0.5,
# main = "RNASeq2GeneNorm data\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5")GGM.R2Gn.min = as.data.frame(GGMnetworkStats(P0.R2Gn.min$sparseParCor, as.table = T))
GGM.R2Gn.min.order = GGM.R2Gn.min[order(GGM.R2Gn.min$degree, decreasing = T), ]
#Output top 5%
GGM.R2Gn.min.order[1:round(nrow(GGM.R2Gn.min.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos mutualInfo
## GAPDH.R2Gn 72 3220.2971 0.004784689 0.9679823 39 33 1.3162098
## SQSTM1.R2Gn 63 1610.4899 0.004504505 1.0000000 38 25 0.8998836
## FN1.R2Gn 54 1316.7827 0.004366812 0.8736875 34 20 1.0721061
## EEF2.R2Gn 48 898.9281 0.004219409 0.8644423 33 15 0.6452620
## HSPA1A.R2Gn 45 1078.3335 0.004132231 0.6867476 26 19 0.4644268
## IGFBP2.R2Gn 41 716.5096 0.004166667 0.8230592 24 17 0.2548038
## RPS6.R2Gn 41 777.9986 0.004115226 0.7749444 23 18 0.3923880
## SYP.R2Gn 35 568.3648 0.004000000 0.6987146 21 14 0.2315599
## CTNNB1.R2Gn 34 481.7430 0.004000000 0.6849627 15 19 0.3528233
## TGM2.R2Gn 32 521.3536 0.003816794 0.5132311 17 15 0.1252101
## variance partialVar
## GAPDH.R2Gn 3.729260 1
## SQSTM1.R2Gn 2.459317 1
## FN1.R2Gn 2.921526 1
## EEF2.R2Gn 1.906487 1
## HSPA1A.R2Gn 1.591102 1
## IGFBP2.R2Gn 1.290208 1
## RPS6.R2Gn 1.480512 1
## SYP.R2Gn 1.260565 1
## CTNNB1.R2Gn 1.423080 1
## TGM2.R2Gn 1.133387 1ggplot(GGM.R2Gn.min.order, aes(x = reorder(rownames(GGM.R2Gn.min.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.R2Gn.min.order$degree), linetype = "dashed", color = "red") +
# 10th unit: top 5%
geom_hline(yintercept = GGM.R2Gn.min.order[10,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-14")
FDRcut 1-1e-6:
#P0.R2Gn = sparsify(P.R2Gn, threshold = "localFDR")
P0.R2Gn.6 = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=1-1e-6)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 1057
## - Corresponding to 5.42 % of possible edges
## #dev.new(width = 20, height = 20, unit = "in", noRstudioGD = F)
set.seed(42)
Ugraph(P0.R2Gn.6$sparseParCor, type = "fancy", lay = "layout_with_fr",
Vsize = 5, Vcex = .45, prune = T, cut = 0.5,
main = "RNASeq2GeneNorm data\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] -0.54152185 -2.6823208069
## [2,] -2.52777380 -0.9635369236
## [3,] -3.05115118 -4.2528146562
## [4,] -2.95857085 0.2119218441
## [5,] 0.77177001 2.0241902056
## [6,] -1.25826919 -1.9276715923
## [7,] -1.46704434 -2.4584398868
## [8,] -0.01644352 -2.2694925021
## [9,] 1.88107426 -1.3369058166
## [10,] 0.70227003 0.6537194278
## [11,] 1.79361320 1.8540098197
## [12,] -1.57504079 -1.1550441053
## [13,] -0.55256920 -8.2462418130
## [14,] -0.68199316 0.0105587104
## [15,] 1.31064839 -2.8938326668
## [16,] 0.20789303 0.9945805013
## [17,] 0.23113071 -0.3722112387
## [18,] 2.98251684 -2.0040443205
## [19,] -0.30318210 -0.0457155418
## [20,] -1.19465049 -0.7169467463
## [21,] -4.47290978 0.9286987221
## [22,] -2.86993539 -3.0618902255
## [23,] -1.87386037 -5.4775653196
## [24,] -6.80893003 -4.4201979000
## [25,] -1.82940988 -2.9495552763
## [26,] -5.13868487 -6.3805145592
## [27,] -2.47785370 2.9271206834
## [28,] 0.09530713 -1.2930458476
## [29,] -1.32116678 1.3963664081
## [30,] -0.33929751 -1.5718390303
## [31,] -1.72998663 -3.7648302178
## [32,] -1.95456886 -5.0583717787
## [33,] -1.07886360 -0.2433476557
## [34,] 1.21739957 3.4123583074
## [35,] -0.03617951 0.0006653903
## [36,] 3.11565080 -0.3101869932
## [37,] -0.52942452 -5.2932545909
## [38,] -0.24301804 2.5318501893
## [39,] -0.41966266 -3.9781673638
## [40,] -0.56044392 -1.9045855333
## [41,] 2.98683827 -2.7721897195
## [42,] 0.13354246 -5.0826360771
## [43,] 0.29914527 -2.1106055036
## [44,] -2.34578189 -0.5641559609
## [45,] -1.62061441 2.0141771405
## [46,] -0.93381569 -2.0159926933
## [47,] -6.55148983 -3.8656619486
## [48,] 2.48196145 -1.2651950180
## [49,] -2.49506535 -4.2015907086
## [50,] 0.26562212 -1.6787891582
## [51,] -0.11795628 -0.8310228538
## [52,] -1.19193043 -1.1537200177
## [53,] -0.46205555 -0.6538650705
## [54,] -1.47106555 -8.2627651612
## [55,] 4.24138739 3.5975322019
## [56,] 0.24879690 -3.0786944686
## [57,] -1.24208901 2.7493399030
## [58,] -0.68098739 1.4652636658
## [59,] -2.80951341 1.9310559211
## [60,] -3.89162157 -2.7526294246
## [61,] -1.05622844 -5.9625201651
## [62,] 3.21680685 0.4605173074
## [63,] -0.34252056 0.7742189183
## [64,] -0.75069256 -1.4889781471
## [65,] -2.64766260 -5.3806136977
## [66,] 1.32274812 -3.3675485667
## [67,] 0.79128900 -1.1224682614
## [68,] -4.14037647 0.0647679289
## [69,] -2.07840797 -1.6983500542
## [70,] -6.98987960 -3.0289175708
## [71,] -0.67312227 -1.1121871636
## [72,] 1.25425598 -1.4892856357
## [73,] -0.35599800 -1.3051768950
## [74,] -1.29272200 -2.8823637510
## [75,] 1.19378453 -1.1274965103
## [76,] -1.88101594 4.6381942949
## [77,] 0.77338735 -0.6619381221
## [78,] -2.47655152 -6.4087288040
## [79,] 1.90480742 0.7639775177
## [80,] 2.32751458 0.6888319314
## [81,] 2.44532612 3.9103923661
## [82,] -2.66677269 -1.9521229006
## [83,] -3.91051014 -0.6369557498
## [84,] 0.28582715 3.2972232391
## [85,] -3.54335215 -5.8653674237
## [86,] -0.87146060 0.5429376428
## [87,] 0.79703896 0.1614962640
## [88,] -5.72147738 -5.3678421398
## [89,] 5.73136797 -1.8096359041
## [90,] -1.62079179 -0.7734014230
## [91,] -2.41095358 -0.0840959274
## [92,] 1.65197729 -0.8232167029
## [93,] 1.50275383 -1.7033391308
## [94,] -6.22178429 -4.6512981765
## [95,] -0.01419192 -3.4595637009
## [96,] 4.20569029 -4.8005331415
## [97,] 1.88783310 3.2190467238
## [98,] 1.61093260 -2.1235585474
## [99,] 1.15530272 -5.0175504792
## [100,] -1.27925113 -1.4054450396
## [101,] 5.68083930 -0.8689934738
## [102,] 0.59140492 -3.0707913772
## [103,] 0.29489957 1.4626839720
## [104,] -3.62488858 -1.7993539369
## [105,] -0.11825778 -1.1128329498
## [106,] -2.59737334 -1.4846197774
## [107,] 0.09750354 -1.7803741189
## [108,] -7.15666422 -1.7387883539
## [109,] -1.96145816 -1.2258137423
## [110,] 1.62792206 -2.8118455040
## [111,] -1.20437507 0.2060277829
## [112,] -4.06301224 1.3998501052
## [113,] -2.25507520 -2.2971490491
## [114,] 2.08363693 0.1280508913
## [115,] -3.19753529 -1.2259728031
## [116,] 1.01385901 -3.6024542468
## [117,] 1.03583880 -1.8844715362
## [118,] 0.56500529 -1.6796871408
## [119,] 2.13440862 -2.7769275708
## [120,] 3.51821601 4.2884951834
## [121,] -2.00389195 0.2613315964
## [122,] 4.81595674 2.8790419751
## [123,] -5.52449180 -1.2519286559
## [124,] -3.96213008 -5.4312565076
## [125,] 0.34941861 -8.0579727156
## [126,] -0.29908740 -2.3403212844
## [127,] -2.49557496 0.4270806578
## [128,] -6.20022833 1.0154493420
## [129,] -3.03236844 -0.5995868287
## [130,] -1.21482706 -4.4226533003
## [131,] 0.55552673 -4.7713096389
## [132,] 1.33029235 -0.5465351731
## [133,] -0.76180523 -0.7582361408
## [134,] -1.57069802 0.4812795790
## [135,] -0.95197681 -0.6424997540
## [136,] -0.81524621 -4.1965403178
## [137,] -0.21907953 -4.4790369294
## [138,] -1.48909466 -0.1524450945
## [139,] -0.96981365 -1.7081745475
## [140,] -0.94318144 -1.3007555819
## [141,] -1.02067079 3.5921937998
## [142,] -7.19607299 -2.4547776491
## [143,] -2.10502718 1.2054471822
## [144,] -3.07111845 -6.2098303825
## [145,] 5.59897609 0.1129115316
## [146,] 0.34154150 -1.2581283900
## [147,] -0.65362164 -0.3267062350
## [148,] -0.12704383 -0.4956486219
## [149,] -4.92774144 -0.3883788376
## [150,] -0.48399089 5.0538272899
## [151,] 0.31743639 -0.8987091347
## [152,] -0.98367407 -3.3769310106
## [153,] 0.73660784 -2.4919698847
## [154,] -0.88141404 -2.9538753068
## [155,] -4.81621089 0.3836162200
## [156,] -6.37001615 -5.2412257207
## [157,] 3.53134584 -1.3125198669
## [158,] 1.31447181 0.1078887007
## [159,] -3.59773339 -4.1503755350
## [160,] -0.88975851 -2.5694290561
## [161,] -5.68199536 1.8730719011
## [162,] -2.17007197 -2.7082810599
## [163,] 3.54839980 -5.6599256062
## [164,] -4.96289378 2.6463412417
## [165,] 0.41091557 -0.1323433783
## [166,] 2.39187855 -0.6056031736
## [167,] -5.42153453 -1.8935913797
## [168,] 1.17270275 1.5934372433
## [169,] -5.25087446 -2.5711102249
## [170,] -5.73090333 -5.9594247452
## [171,] 2.10286762 -4.7523998603
## [172,] -1.78090605 -1.9311695168
## [173,] -0.05846189 -2.1083750323
## [174,] -7.11129446 -3.6086269930#Ugraph(P0.R2Gn.6$sparseParCor, type = "fancy", lay = "layout_in_circle",
# Vsize = 5, Vcex = .5, prune = T, cut = 0.5,
# main = "RNASeq2GeneNorm data\nFDRcutoff at .999999, Strong Edge cutoff at 0.5")GGM.R2Gn.6 = as.data.frame(GGMnetworkStats(P0.R2Gn.6$sparseParCor, as.table = T))
GGM.R2Gn.6.order = GGM.R2Gn.6[order(GGM.R2Gn.6$degree, decreasing = T), ]
#Output top 5%
GGM.R2Gn.6.order[1:round(nrow(GGM.R2Gn.6.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos mutualInfo
## GAPDH.R2Gn 112 4702.3715 0.004273504 1.0000000 56 56 1.3594455
## SQSTM1.R2Gn 81 1534.4196 0.003690037 0.8962939 47 34 0.9259119
## FN1.R2Gn 74 1602.9597 0.003636364 0.8018610 46 28 1.0805192
## HSPA1A.R2Gn 70 1370.0239 0.003597122 0.7250416 38 32 0.4884313
## EEF2.R2Gn 63 762.6757 0.003521127 0.7983647 38 25 0.6501149
## IGFBP2.R2Gn 62 1199.7673 0.003521127 0.7852547 35 27 0.2716628
## SYP.R2Gn 61 1285.7088 0.003436426 0.6896222 35 26 0.2486661
## RPS6.R2Gn 60 1146.1182 0.003460208 0.7277443 34 26 0.4176465
## TGM2.R2Gn 55 1075.7374 0.003355705 0.6095397 31 24 0.1507605
## CTNNB1.R2Gn 50 324.2004 0.003344482 0.7179654 23 27 0.3197247
## variance partialVar
## GAPDH.R2Gn 3.894034 1
## SQSTM1.R2Gn 2.524169 1
## FN1.R2Gn 2.946209 1
## HSPA1A.R2Gn 1.629758 1
## EEF2.R2Gn 1.915761 1
## IGFBP2.R2Gn 1.312144 1
## SYP.R2Gn 1.282314 1
## RPS6.R2Gn 1.518384 1
## TGM2.R2Gn 1.162718 1
## CTNNB1.R2Gn 1.376749 1ggplot(GGM.R2Gn.6.order, aes(x = reorder(rownames(GGM.R2Gn.6.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.R2Gn.6.order$degree), linetype = "dashed", color = "red") +
# 10th unit: top 5%
geom_hline(yintercept = GGM.R2Gn.6.order[10,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-6")
C. miRNASeqGene
data.mRSG = data.numeric[, group.mRSG]set.seed(42)
opt.mRSG = optPenalty.kCVauto(Y = data.mRSG, lambdaMin = 1e-11, lambdaMax = 10)
#opt.mRSG.10 = optPenalty.kCVauto(Y = data.mRSG, lambdaMin = 1e-11, lambdaMax = 1000, fold = 10)
#opt.mRSG.5 = optPenalty.kCVauto(Y = data.mRSG, lambdaMin = 1e-11, lambdaMax = 1000, fold = 5)
#opt.mRSG.3 = optPenalty.kCVauto(Y = data.mRSG, lambdaMin = 1e-11, lambdaMax = 1000, fold = 3)
#setNames(c(3,5,10,43), c(opt.mRSG.3$optLambda, opt.mRSG.5$optLambda, opt.mRSG.10$optLambda, opt.mRSG$optLambda))
opt.mRSG$optLambda## [1] 3.5931edgeHeat(opt.mRSG$optPrec, diag = F, textsize = 0.1)## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
CNplot(covML(data.mRSG),
lambdaMin = 1e-11,
lambdaMax = 1000,
step = 5000,
Iaids = T,
vertical = T,
value = opt.mRSG$optLambda)## Perform input checks...
## Calculating spectral condition numbers...
## Calculating interpretational aids...
## Plotting...
i = 0.1
edgeHeat(M = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 16050
## - Corresponding to 14.56 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.5
edgeHeat(M = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 16049
## - Corresponding to 14.56 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.9
edgeHeat(M = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 10494
## - Corresponding to 9.52 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.999
edgeHeat(M = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 5669
## - Corresponding to 5.14 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 1-1e-6
edgeHeat(M = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 3091
## - Corresponding to 2.8 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 1-1e-10
edgeHeat(M = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 1571
## - Corresponding to 1.43 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
Smallest possible FDRcut:
P0.mRSG.min = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=1-1e-13)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 1164
## - Corresponding to 1.06 % of possible edges
## #dev.new(width = 20, height = 20, unit = "in", noRStudioGD = F)
set.seed(42)
Ugraph(P0.mRSG.min$sparseParCor, type = "fancy", lay = "layout_with_fr",
Vsize = 2, Vcex = .3, prune = T, cut = 0.5,
main = "miRNASeqGene data\nFDRcutoff at 1-1e-13, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] 1.693443199 -0.57080262
## [2,] 2.237906712 2.09902249
## [3,] 3.407380042 -5.22942842
## [4,] 1.555401960 8.09915341
## [5,] 1.709161949 6.81248248
## [6,] 0.193112853 5.41140659
## [7,] 0.626934990 7.33240361
## [8,] 1.062255142 -1.33596998
## [9,] -4.448723695 -6.34017023
## [10,] -0.172648992 -2.91921698
## [11,] 2.929827339 -2.70393521
## [12,] 2.731830830 -1.31063492
## [13,] 2.480043823 -2.16720867
## [14,] 1.506613757 0.78739724
## [15,] 3.342913606 0.50705997
## [16,] 3.769793891 -7.91127051
## [17,] 0.144797115 -1.38872733
## [18,] 1.523180674 -5.72488991
## [19,] 3.555983344 -4.59337048
## [20,] 7.932228615 -1.47770407
## [21,] 3.876073986 -1.32245826
## [22,] 4.220382868 11.01844206
## [23,] 0.922281428 -2.65403836
## [24,] 0.338775353 -0.48257555
## [25,] -1.843727463 -7.50665268
## [26,] -0.245275929 -3.59795509
## [27,] 0.478563530 -2.14775787
## [28,] -4.916303015 -1.25334284
## [29,] 5.270168775 0.71996878
## [30,] 5.413851056 -2.10286016
## [31,] -1.074650119 1.00258047
## [32,] 4.295841529 -1.44737800
## [33,] 1.235885923 -0.79711259
## [34,] 0.483524823 11.92959079
## [35,] -1.755776857 -2.69667657
## [36,] 0.106001345 -1.23126557
## [37,] 2.020007889 -2.13964333
## [38,] 1.954864933 0.81986277
## [39,] 1.305955754 -1.93140878
## [40,] -2.560407144 -4.32781082
## [41,] 2.657876730 -5.61609491
## [42,] 4.256820591 -2.06689018
## [43,] 4.185609255 -2.69985537
## [44,] 2.674719600 11.60794630
## [45,] 7.877213374 -2.73460091
## [46,] 7.752747608 -3.25043782
## [47,] 2.764932109 -3.91847665
## [48,] 1.135116433 -0.04564735
## [49,] 1.831904149 -1.18212127
## [50,] 3.672487656 -4.19031439
## [51,] 0.725085363 -7.07274673
## [52,] 0.559091692 -4.19962146
## [53,] 4.635782268 -0.19043058
## [54,] 4.976871053 -0.40317870
## [55,] 5.883221520 2.42843790
## [56,] -3.805324199 -7.14143008
## [57,] -0.463975043 -0.40813107
## [58,] 1.704746789 -0.19973475
## [59,] -1.505940255 4.09356561
## [60,] 4.541633935 -4.71061109
## [61,] 4.642847312 -1.21866239
## [62,] 3.166927202 -2.98713521
## [63,] 6.382069150 -2.83345514
## [64,] 1.451638055 -3.75664978
## [65,] -0.160331203 -8.48275551
## [66,] -4.857495091 -7.94506850
## [67,] -2.977600130 1.74927695
## [68,] 3.117979787 -1.42110210
## [69,] 1.755911753 11.94739499
## [70,] 9.554642481 -0.79680118
## [71,] -1.370797128 2.19547402
## [72,] 3.206155709 -0.60653995
## [73,] 4.381143027 3.07098637
## [74,] 2.752478935 -0.92508226
## [75,] 4.600376978 -2.79221010
## [76,] 2.688718387 -7.84606628
## [77,] 4.706348662 0.47035676
## [78,] 3.722117182 -2.96524542
## [79,] 6.861715440 -2.74050991
## [80,] 3.392814164 -2.41594243
## [81,] -1.263001179 -0.25523443
## [82,] -0.957840415 -3.53071024
## [83,] 9.290424913 -2.88550368
## [84,] 6.637910365 0.09805605
## [85,] 0.282580671 7.84794587
## [86,] 0.607715259 6.82903672
## [87,] 1.658184651 7.52617151
## [88,] 2.011181295 8.07309164
## [89,] 1.084720155 -7.84262884
## [90,] 3.870888550 -0.53776579
## [91,] 3.089024228 -0.24638481
## [92,] 7.043067346 1.07296584
## [93,] 4.661463875 2.22487268
## [94,] 5.604696526 -1.64413980
## [95,] 1.241780051 -2.63063958
## [96,] 7.155831118 -0.02139164
## [97,] -3.041458248 -0.98746340
## [98,] 4.332788080 -0.74539708
## [99,] 2.562601394 0.29992003
## [100,] -2.702801326 -3.62890579
## [101,] 4.757957822 -3.57384821
## [102,] 5.318674016 -0.21582119
## [103,] -0.306503185 -1.80105732
## [104,] 2.335515010 1.62400881
## [105,] 3.228864585 11.87412677
## [106,] 2.238078016 -0.01757222
## [107,] 0.766209042 8.09272485
## [108,] 2.154260900 6.01749149
## [109,] -0.220969140 8.36103334
## [110,] 1.708339189 10.08193479
## [111,] 2.320206733 12.18355246
## [112,] 1.706886013 5.04153924
## [113,] 3.680257066 11.38944241
## [114,] 3.874244828 -3.32630792
## [115,] -0.868659975 -8.13433337
## [116,] 2.945516268 1.00790350
## [117,] 4.640814727 -1.05758891
## [118,] -2.898815235 0.04405493
## [119,] -0.579989935 -1.52584987
## [120,] 11.546254989 1.12457254
## [121,] 1.409420971 -1.45182563
## [122,] 1.079349236 -4.89749479
## [123,] -0.002205569 10.29975248
## [124,] 0.107659318 9.61297171
## [125,] 2.619790033 8.49024492
## [126,] 4.064807815 -1.88072447
## [127,] -0.469648172 9.47639283
## [128,] 0.754893463 9.78219618
## [129,] 1.533724072 8.76880223
## [130,] -7.731890653 -10.63190670
## [131,] 2.553572639 -1.80544121
## [132,] 7.637007963 -0.59366945
## [133,] 2.272020802 7.67671130
## [134,] 2.142355656 5.45598138
## [135,] -0.708802397 7.73625379
## [136,] -0.456762688 -3.85651156
## [137,] 1.683480550 -1.38007925
## [138,] 2.541826351 -0.66117754
## [139,] 2.997766036 7.57045921
## [140,] -0.644109662 8.13114046
## [141,] 3.612379558 0.54673109
## [142,] 3.395682491 -1.98482160
## [143,] 6.052100427 -7.04307170
## [144,] 5.790531676 -5.76182106
## [145,] 5.827128240 -7.60365827
## [146,] 6.567793618 -7.02813830
## [147,] 5.425040378 -7.41032057
## [148,] 5.645198893 -7.02292983
## [149,] 5.513668667 -5.24743281
## [150,] 3.542104180 -14.25929494
## [151,] 3.214957578 -10.21575421
## [152,] 5.793915311 -6.14595426
## [153,] 4.930717567 -5.38806309
## [154,] 5.729014031 -7.33260723
## [155,] 6.414362497 -7.30532295
## [156,] 6.132320767 -7.42063184
## [157,] 6.224813429 -6.78382695
## [158,] 6.570006789 -6.72221557
## [159,] 5.201267290 -6.37297894
## [160,] 0.626348251 0.80287623
## [161,] 0.458709470 1.04634835
## [162,] -0.396923465 0.91254269
## [163,] 0.065895725 -0.20758829
## [164,] -0.009743296 0.72287473
## [165,] 0.843042047 0.27758995
## [166,] 0.416367487 0.09223111
## [167,] 0.554479507 -1.05546457
## [168,] 1.249217141 0.34368532
## [169,] 1.487445222 1.05363004
## [170,] -0.046532072 1.16244980
## [171,] 0.731059749 1.73561962
## [172,] 1.151272809 12.06464551
## [173,] 1.513938976 5.90358210
## [174,] 2.178186474 9.53834294
## [175,] -1.185113684 3.30272763
## [176,] 3.474649694 -0.65711384
## [177,] -0.533116194 -1.15764459
## [178,] 3.550514878 0.99227045
## [179,] 6.583120538 2.06572140
## [180,] -3.662881683 3.15187027
## [181,] 1.959515575 -4.15748737
## [182,] 3.544594236 -1.29712855
## [183,] -0.880797308 -3.06522674
## [184,] -0.357119026 0.29930478
## [185,] 1.894490182 1.53473625
## [186,] 0.695487399 -5.46643765
## [187,] 4.871793528 -2.43320663
## [188,] 1.009904588 -3.52529183
## [189,] 2.222657873 -2.73332602
## [190,] -0.675412188 -2.53823794
## [191,] -0.231433075 -4.76433392
## [192,] 2.526087060 -0.13525751
## [193,] -2.978762931 -2.94687907
## [194,] 3.625982865 -0.12089297
## [195,] -1.618413290 1.21595641
## [196,] 2.650432630 7.58538456
## [197,] 0.730032812 4.90822816
## [198,] 1.524041871 -2.13631553
## [199,] 1.544957311 1.82042509
## [200,] 1.056801680 7.10246900
## [201,] -1.399463427 -1.87770210
## [202,] -1.600010259 -4.72401448
## [203,] 0.144541576 -3.27623448
## [204,] 1.850548707 -4.94678203
## [205,] -0.309338808 11.65286150
## [206,] 9.477822551 -0.04472504
## [207,] 6.155125804 -5.01871841
## [208,] 2.844011794 0.01885118
## [209,] 1.112513667 6.16222381
## [210,] 2.046533466 -2.96018731
## [211,] 1.199982913 -3.74148441
## [212,] 2.460075086 -2.96587265
## [213,] 0.623577260 10.60787870
## [214,] 2.541392913 -1.52506846
## [215,] -0.860040405 -1.72733951
## [216,] -0.953536526 -1.33114546
## [217,] 0.454321625 -2.67903351
## [218,] 6.331504710 -3.83099014
## [219,] 2.385118000 -4.20497553
## [220,] -0.940924329 -5.44626448
## [221,] 1.289219269 -9.07911134
## [222,] 9.011017639 -4.03378955#Ugraph(P0.mRSG.min$sparseParCor, type = "fancy", lay = "layout_in_circle",
# Vsize = 2, Vcex = .1, prune = T, cut = 0.5,
# main = "miRNASeqGene data\nFDRcutoff at 1-1e-13, Strong Edge cutoff at 0.5")GGM.mRSG.min = as.data.frame(GGMnetworkStats(P0.mRSG.min$sparseParCor, as.table = T))
GGM.mRSG.min.order = GGM.mRSG.min[order(GGM.mRSG.min$degree, decreasing = T), ]
#Output top 5%
GGM.mRSG.min.order[1:round(nrow(GGM.mRSG.min.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos
## hsa-mir-206 46 3024.5913 0.002304147 1.0000000 20 26
## hsa-mir-329-1 42 3788.8954 0.001855288 0.2068821 3 39
## hsa-mir-1258 38 2389.8497 0.002096436 0.7516007 13 25
## hsa-mir-137 35 2091.5612 0.002092050 0.5821485 20 15
## hsa-mir-135a-2 31 861.9311 0.001964637 0.6824739 17 14
## hsa-mir-873 29 978.3604 0.002008032 0.5695108 15 14
## hsa-mir-1251 28 877.2843 0.001996008 0.4894875 9 19
## hsa-mir-519a-1 27 425.7017 0.001937984 0.7593073 11 16
## hsa-mir-668 27 974.1874 0.001769912 0.1811547 1 26
## hsa-mir-3923 26 1163.8327 0.001941748 0.4080900 13 13
## hsa-mir-1197 23 307.8578 0.001587302 0.1400930 1 22
## hsa-mir-218-1 23 1135.4310 0.001930502 0.3332649 8 15
## hsa-mir-3934 23 329.0913 0.001893939 0.5246196 14 9
## hsa-mir-526b 23 1130.7760 0.002032520 0.6506809 8 15
## hsa-mir-9-3 23 411.3859 0.001956947 0.4853357 8 15
## hsa-mir-122 22 358.2695 0.001919386 0.3919011 17 5
## hsa-mir-488 22 1132.1611 0.001851852 0.4008020 6 16
## hsa-mir-133b 21 195.0829 0.001923077 0.6710586 7 14
## hsa-mir-518c 21 137.8757 0.001872659 0.6254225 5 16
## hsa-mir-520a 21 331.9399 0.001941748 0.6361110 7 14
## hsa-mir-124-3 20 356.7712 0.001949318 0.3930271 11 9
## hsa-mir-153-1 20 651.5283 0.001828154 0.3036387 10 10
## hsa-mir-429 20 361.3372 0.001788909 0.2942850 10 10
## hsa-mir-510 20 238.2183 0.001686341 0.4289051 2 18
## mutualInfo variance partialVar
## hsa-mir-206 0.03292890 1.033477 1
## hsa-mir-329-1 0.02103435 1.021257 1
## hsa-mir-1258 0.02247154 1.022726 1
## hsa-mir-137 0.02237436 1.022627 1
## hsa-mir-135a-2 0.02351349 1.023792 1
## hsa-mir-873 0.01980087 1.019998 1
## hsa-mir-1251 0.02147339 1.021706 1
## hsa-mir-519a-1 0.01763995 1.017796 1
## hsa-mir-668 0.01319329 1.013281 1
## hsa-mir-3923 0.01784204 1.018002 1
## hsa-mir-1197 0.01172850 1.011798 1
## hsa-mir-218-1 0.01380980 1.013906 1
## hsa-mir-3934 0.01104792 1.011109 1
## hsa-mir-526b 0.01837847 1.018548 1
## hsa-mir-9-3 0.01562936 1.015752 1
## hsa-mir-122 0.01418258 1.014284 1
## hsa-mir-488 0.01150804 1.011575 1
## hsa-mir-133b 0.01022348 1.010276 1
## hsa-mir-518c 0.01146126 1.011527 1
## hsa-mir-520a 0.01381192 1.013908 1
## hsa-mir-124-3 0.01160858 1.011676 1
## hsa-mir-153-1 0.01621961 1.016352 1
## hsa-mir-429 0.01472198 1.014831 1
## hsa-mir-510 0.01805642 1.018220 1ggplot(GGM.mRSG.min.order, aes(x = reorder(rownames(GGM.mRSG.min.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.mRSG.min.order$degree), linetype = "dashed", color = "red") +
# 24th unit: top 5%
geom_hline(yintercept = GGM.mRSG.min.order[24,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "miRNASeqGene", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-13")
FDRcut 1-1e-6:
P0.mRSG.6 = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=1-1e-6)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 3091
## - Corresponding to 2.8 % of possible edges
## #dev.new(width = 20, height = 20, unit = "in", noRStudioGD = F)
set.seed(42)
Ugraph(P0.mRSG.6$sparseParCor, type = "fancy", lay = "layout_with_fr",
Vsize = 2, Vcex = .3, prune = T, cut = 0.5,
main = "miRNASeqGene data\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] 2.0414684168 -8.264597744
## [2,] -1.1675039128 -0.804889250
## [3,] 0.3738857418 -0.015780581
## [4,] -7.6410330380 -0.809465400
## [5,] -1.1747619636 -2.810894145
## [6,] -0.0553588813 3.377971773
## [7,] 0.3556158150 3.430412961
## [8,] 0.8550540726 2.929747388
## [9,] -0.0066746246 2.842760487
## [10,] -1.7573468955 -1.086597035
## [11,] -3.0743731826 0.119128961
## [12,] 5.5791247353 0.019243822
## [13,] -2.8713371630 -2.963344915
## [14,] -0.6501123290 -0.489293054
## [15,] -0.8544911024 -0.140814506
## [16,] 0.3791833849 -1.071472196
## [17,] 1.0789755654 -0.712184425
## [18,] 1.1339235668 -1.443850134
## [19,] -2.2190761068 0.540850912
## [20,] 0.1381562594 -4.959376622
## [21,] -1.2479752378 -1.783118714
## [22,] 5.3333655774 2.537516467
## [23,] -0.0745066446 -0.774956100
## [24,] -0.2154185758 -3.111419427
## [25,] -6.0374036651 -6.208451635
## [26,] -5.8630824151 2.286409914
## [27,] 2.7056766352 0.317901032
## [28,] 4.2881122126 -6.252083270
## [29,] 0.1333581179 -2.972601754
## [30,] -0.2400353602 4.494989024
## [31,] -0.6099603091 -1.945629177
## [32,] 0.7002497945 -1.856510234
## [33,] 5.2210908234 -1.748226857
## [34,] 0.8077229883 -3.805736203
## [35,] -4.2634883097 4.022959612
## [36,] -2.6219465219 -1.255522677
## [37,] -1.4241297913 -0.995201507
## [38,] 3.7363075378 -4.268025369
## [39,] 1.9819929165 -0.994949976
## [40,] 1.6923614249 -1.600121968
## [41,] 0.5500713221 -1.188848968
## [42,] 1.3415271045 -2.017360687
## [43,] -0.9117713617 -0.625916630
## [44,] -0.9573859298 3.446627176
## [45,] -2.3625170954 -2.345863661
## [46,] -0.3901344641 -2.015979868
## [47,] -0.4971318960 -3.350777275
## [48,] 1.3050609848 4.287663696
## [49,] -0.9497960787 -0.391940819
## [50,] -0.2681666138 -0.587419652
## [51,] -1.7074125705 -1.975483852
## [52,] -0.8596130480 -4.058596819
## [53,] 4.9435163511 -3.079084254
## [54,] 15.5220345641 -4.839944216
## [55,] -4.6596152965 -3.248935822
## [56,] 0.5393476886 -0.703085160
## [57,] 0.0355622502 -0.057826935
## [58,] 0.7861810955 4.474663400
## [59,] -1.6109894781 -5.731145498
## [60,] -1.4692371715 -4.729888895
## [61,] -0.2792236238 -2.356392783
## [62,] -0.5901011139 -1.639751993
## [63,] -1.1934251111 -0.273454841
## [64,] -1.2189665635 -3.408148606
## [65,] -6.9821803786 0.569032430
## [66,] -7.1553152715 -2.227137887
## [67,] -7.1328450358 -1.367524868
## [68,] 1.2097372225 -5.599214458
## [69,] -2.8111004365 -3.256614771
## [70,] -2.1292297619 -2.717245801
## [71,] -4.1705861727 -4.377505560
## [72,] 1.2387215322 -1.022510500
## [73,] 1.4923005846 -1.109450542
## [74,] -0.6042237611 -4.999963897
## [75,] -1.8984542386 -7.022147214
## [76,] -2.2063189909 -2.203561608
## [77,] -0.6569417206 -0.252184140
## [78,] 0.1009427178 -0.871591006
## [79,] -4.8294714092 1.288730952
## [80,] 1.2990234699 -2.834457668
## [81,] -7.0615453994 -0.415473104
## [82,] 0.0860617183 -2.229224588
## [83,] -0.0992084121 -2.616832188
## [84,] -1.2602226946 -5.017964764
## [85,] -0.2430583718 -2.943004023
## [86,] -0.3118160257 -8.518090213
## [87,] -3.5158535239 -3.065109175
## [88,] -1.5400679328 0.987154534
## [89,] 0.4700294908 -1.521581902
## [90,] -0.4039027199 5.079982147
## [91,] 6.3927378365 -4.655290648
## [92,] -3.2929410293 -1.911731213
## [93,] -5.7747009586 -5.426782462
## [94,] 2.2337601603 1.547675182
## [95,] 0.1654252195 -1.441542534
## [96,] -1.0330749507 -4.393912887
## [97,] 0.1177007436 -0.652798211
## [98,] 0.1431657991 -3.325810186
## [99,] -2.4086044250 -4.688758424
## [100,] -0.0902170349 -3.550410242
## [101,] 0.2848442711 -2.038357626
## [102,] -0.4706244642 -1.410269886
## [103,] -0.6395980633 -2.878165762
## [104,] -0.9253927986 -1.413665674
## [105,] 0.5141657488 -2.799393371
## [106,] 0.6495681115 -0.354423852
## [107,] 3.0877219378 -6.427137969
## [108,] 3.1355083924 -1.219808758
## [109,] 1.8998627465 -0.121590574
## [110,] 0.2284460421 4.627374302
## [111,] 0.4320360889 4.537154650
## [112,] 0.2221997015 2.733261701
## [113,] 0.9491350496 2.674642496
## [114,] 0.6518099839 -8.627015264
## [115,] -5.3857382467 -2.893471842
## [116,] 2.6290820063 5.638039834
## [117,] 0.2440528469 -2.485968443
## [118,] -0.0533674238 -1.547254046
## [119,] 1.4237381739 0.330659707
## [120,] 0.5531226041 1.136820015
## [121,] 1.6160394815 -0.590845992
## [122,] -0.7772932472 -2.312770444
## [123,] 1.5570888376 -2.962788291
## [124,] -3.8812771068 -4.817111214
## [125,] 0.4645870550 -5.515493674
## [126,] -0.9532329371 -3.197070019
## [127,] 1.3184286339 -3.064978857
## [128,] -0.5345626016 -2.583183193
## [129,] -0.9166055715 -3.634812833
## [130,] -0.6332352198 -1.137613111
## [131,] -0.4446501908 -0.868166445
## [132,] -1.3833937935 -2.361845740
## [133,] -0.8910565547 4.309277751
## [134,] -1.1316230754 -2.262072021
## [135,] -0.7856089778 4.084515918
## [136,] 0.5539668706 -2.136386014
## [137,] 1.0051099353 4.003607382
## [138,] 0.3859054227 3.097900311
## [139,] 1.1307625246 4.893642482
## [140,] 0.9872217295 4.599804025
## [141,] -1.1886168818 3.841829832
## [142,] -0.4366360618 4.543182166
## [143,] -0.1658837592 3.030492965
## [144,] 0.3482802045 5.110401099
## [145,] 1.2513054291 3.805190444
## [146,] 1.1712628110 -2.235557316
## [147,] 2.9517305276 -1.577270767
## [148,] -5.3263581342 3.197981934
## [149,] 1.2624809282 -2.553426369
## [150,] -4.3819606673 -1.853276067
## [151,] 1.4317248284 -2.312981531
## [152,] -2.7488523406 -1.473871965
## [153,] 0.2922051977 -0.502726576
## [154,] 2.3870816009 -2.812984745
## [155,] -0.7343235991 -1.719120970
## [156,] -1.1528102666 -2.565321750
## [157,] -0.0601639058 4.673738922
## [158,] 0.1146378355 4.409006515
## [159,] 0.3515271973 3.884927151
## [160,] -1.3314963048 4.620142649
## [161,] 0.9673442554 -0.462674157
## [162,] -0.6333768183 4.540986782
## [163,] -0.4646259414 4.177195700
## [164,] -3.7902223684 1.407688914
## [165,] -0.3535215963 2.451973338
## [166,] 15.7307500564 -3.707477159
## [167,] -6.1688001102 -3.605725994
## [168,] -0.2323559640 -1.527023626
## [169,] 0.6435963454 -3.316657016
## [170,] 0.0242696839 4.169450209
## [171,] 15.8811831967 -2.587194128
## [172,] 0.1330703381 3.674169853
## [173,] 0.8227128901 3.850085260
## [174,] -1.5333074594 -1.336074055
## [175,] 0.0009126745 -1.313294653
## [176,] -0.2830442611 -0.005331427
## [177,] -2.0045304789 3.238047490
## [178,] 0.6362383927 4.299448015
## [179,] -0.0590189810 5.341830245
## [180,] 1.4963422105 4.003302221
## [181,] 0.8791660656 -1.512921601
## [182,] -1.8006217518 -2.631151539
## [183,] 2.4732671323 -4.602225818
## [184,] 1.4533627381 -3.835975431
## [185,] 1.8966622764 -4.674745760
## [186,] 1.9090020304 -4.939378606
## [187,] 2.2342528259 -4.963202840
## [188,] 2.5204518016 -4.841030992
## [189,] 0.9607437181 -3.542577283
## [190,] -3.7218827187 -6.213431612
## [191,] -2.1496463816 -3.845379384
## [192,] 1.1276274959 -4.392832778
## [193,] 1.4077236271 -3.453119117
## [194,] 1.6089292882 -5.094327594
## [195,] 2.1510350267 -5.380987515
## [196,] 2.6431189265 -4.363489739
## [197,] 2.0454251184 -4.412885986
## [198,] 2.2730642794 -4.313568663
## [199,] 1.5669859648 -4.435127984
## [200,] 0.9763873744 -0.223087521
## [201,] -0.9041903200 0.290263297
## [202,] 0.0683976704 0.609910835
## [203,] -0.0876707631 -0.365719678
## [204,] -0.4455327983 0.637346081
## [205,] 0.7089722511 -0.795760565
## [206,] -1.3747940318 0.187037447
## [207,] -0.9970786932 -1.021584408
## [208,] -0.2952073497 -0.308503493
## [209,] 0.4971538006 0.322187756
## [210,] 0.0488483929 0.244603407
## [211,] -0.4924412209 0.311312469
## [212,] 1.0463459058 4.362067410
## [213,] -0.1051974732 2.391490654
## [214,] 0.4095102276 4.212128307
## [215,] 0.8567573257 0.746863254
## [216,] -0.0231174642 -0.989582713
## [217,] -1.5484885294 -2.124600059
## [218,] 1.3651390568 -0.343987384
## [219,] 2.0454217727 -1.673124136
## [220,] -1.0721647731 -7.304154435
## [221,] 6.1390014960 1.742365003
## [222,] -1.2746745749 -2.030703617
## [223,] 0.8523333002 -2.183942681
## [224,] 3.5946345345 -2.939305828
## [225,] -1.9076033626 -0.551695412
## [226,] -0.3100745346 -1.146304092
## [227,] 0.9367146411 -1.791762320
## [228,] 2.5664750330 -1.948848454
## [229,] 4.2766093090 -1.252859878
## [230,] -1.4073458849 -0.359289650
## [231,] 0.0346369415 -1.871651278
## [232,] -0.9904137471 -1.954134757
## [233,] 0.8801871477 -2.971545542
## [234,] -1.7128411296 -3.116223644
## [235,] 0.7757586377 -1.166440659
## [236,] -3.1893104583 -6.583052642
## [237,] -7.7181835438 -1.849811056
## [238,] -2.9738006832 -2.497143776
## [239,] 0.7366906984 -2.522663753
## [240,] -4.8727494470 -1.492810559
## [241,] -1.6845135025 -3.679495017
## [242,] 1.7847597828 3.939927726
## [243,] -0.2847021701 3.780797226
## [244,] 0.0669335536 2.208060878
## [245,] -1.2527266239 -1.564688428
## [246,] 0.2659403489 1.435096079
## [247,] 0.1073706052 2.955113133
## [248,] -0.7698560166 -0.922443720
## [249,] -2.2083386044 -3.357628531
## [250,] -1.9465588735 -1.413884580
## [251,] -0.1839077675 -1.021514098
## [252,] -2.7623357883 -4.555463900
## [253,] 1.5223128603 4.709132963
## [254,] 3.5247908483 -2.356880745
## [255,] -2.4388490296 -0.248880416
## [256,] 0.3374642401 -1.668893155
## [257,] 0.3205690287 2.883236038
## [258,] -2.1608027522 -1.578842311
## [259,] -1.9476607158 -1.843573242
## [260,] -1.1587724673 -1.226317864
## [261,] -1.6766727964 -8.721810211
## [262,] 0.6856142901 4.806806422
## [263,] -0.7093028492 -1.407005840
## [264,] -2.3143877548 -0.772906146
## [265,] -2.4523653148 -0.928807244
## [266,] -1.5022588016 -1.503159813
## [267,] 3.5027698759 -3.437969237
## [268,] 1.1198638170 0.036429072
## [269,] 2.1139520248 -1.229743113
## [270,] 0.0833686859 -4.334733724
## [271,] 3.0262380534 -0.634698177
## [272,] -4.3718378779 -1.142981479
## [273,] -0.4896320601 -6.535422444#Ugraph(P0.mRSG.6$sparseParCor, type = "fancy", lay = "layout_in_circle",
# Vsize = 2, Vcex = .1, prune = T, cut = 0.5,
# main = "miRNASeqGene data\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")GGM.mRSG.6 = as.data.frame(GGMnetworkStats(P0.mRSG.6$sparseParCor, as.table = T))
GGM.mRSG.6.order = GGM.mRSG.6[order(GGM.mRSG.6$degree, decreasing = T), ]
#Output top 5%
GGM.mRSG.6.order[1:round(nrow(GGM.mRSG.6.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos
## hsa-mir-206 74 1239.5984 0.002083333 1.0000000 38 36
## hsa-mir-137 72 2259.8181 0.002096436 0.9202624 39 33
## hsa-mir-1258 69 1835.3466 0.002049180 0.7615516 34 35
## hsa-mir-3923 65 1442.4219 0.001949318 0.6630538 36 29
## hsa-mir-135a-2 63 949.9530 0.001923077 0.7805614 33 30
## hsa-mir-656 61 1236.5778 0.001845018 0.9967304 14 47
## hsa-mir-216b 58 1350.7417 0.001984127 0.6549100 29 29
## hsa-mir-488 58 1184.0701 0.001992032 0.6766936 24 34
## hsa-mir-329-1 57 774.6184 0.001785714 0.9710800 9 48
## hsa-mir-668 56 787.2091 0.001811594 0.9837761 6 50
## hsa-mir-1197 55 564.0291 0.001757469 0.9666753 7 48
## hsa-mir-218-1 53 1037.2767 0.001845018 0.5402034 27 26
## hsa-mir-1185-1 52 343.5733 0.001647446 0.9043083 4 48
## hsa-mir-873 52 1304.6211 0.001953125 0.6201089 30 22
## hsa-mir-1185-2 51 283.4901 0.001666667 0.9273307 4 47
## hsa-mir-665 51 1297.0284 0.001912046 0.8758355 17 34
## hsa-mir-770 51 638.3712 0.001776199 0.9423813 5 46
## hsa-mir-1251 50 432.9843 0.001926782 0.6195067 16 34
## hsa-mir-380 50 460.8525 0.001766784 0.9156204 6 44
## hsa-mir-767 50 931.9401 0.001945525 0.5872044 23 27
## hsa-mir-3166 48 633.1112 0.001831502 0.5249175 26 22
## hsa-mir-376a-2 48 442.7223 0.001760563 0.9137662 4 44
## hsa-mir-31 47 477.2020 0.001848429 0.5364720 29 18
## hsa-mir-383 47 691.4625 0.001908397 0.5459420 27 20
## mutualInfo variance partialVar
## hsa-mir-206 0.03647879 1.037152 1
## hsa-mir-137 0.03038218 1.030848 1
## hsa-mir-1258 0.02875697 1.029174 1
## hsa-mir-3923 0.02582764 1.026164 1
## hsa-mir-135a-2 0.02928413 1.029717 1
## hsa-mir-656 0.01769005 1.017847 1
## hsa-mir-216b 0.02227444 1.022524 1
## hsa-mir-488 0.01846425 1.018636 1
## hsa-mir-329-1 0.02054543 1.020758 1
## hsa-mir-668 0.01728309 1.017433 1
## hsa-mir-1197 0.01636555 1.016500 1
## hsa-mir-218-1 0.02035512 1.020564 1
## hsa-mir-1185-1 0.01356332 1.013656 1
## hsa-mir-873 0.02388268 1.024170 1
## hsa-mir-1185-2 0.01283176 1.012914 1
## hsa-mir-665 0.01249051 1.012569 1
## hsa-mir-770 0.01311216 1.013198 1
## hsa-mir-1251 0.02468907 1.024996 1
## hsa-mir-380 0.01221634 1.012291 1
## hsa-mir-767 0.02432526 1.024624 1
## hsa-mir-3166 0.01694897 1.017093 1
## hsa-mir-376a-2 0.01033047 1.010384 1
## hsa-mir-31 0.01716165 1.017310 1
## hsa-mir-383 0.01957979 1.019773 1ggplot(GGM.mRSG.6.order, aes(x = reorder(rownames(GGM.mRSG.6.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.mRSG.6.order$degree), linetype = "dashed", color = "red") +
# 24th unit: top 5%
geom_hline(yintercept = GGM.mRSG.6.order[24,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "miRNASeqGene", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-6")
D. All data
set.seed(42)
opt.all = optPenalty.kCVauto(Y = data.numeric, lambdaMin = 1e-11, lambdaMax = 10)
#opt.all.10 = optPenalty.kCVauto(Y = data.numeric, lambdaMin = 1e-11, lambdaMax = 1000, fold = 10)
#opt.all.5 = optPenalty.kCVauto(Y = data.numeric, lambdaMin = 1e-11, lambdaMax = 1000, fold = 5)
#opt.all.3 = optPenalty.kCVauto(Y = data.numeric, lambdaMin = 1e-11, lambdaMax = 1000, fold = 3)
#setNames(c(3,5,10,43), c(opt.all.3$optLambda, opt.all.5$optLambda, opt.all.10$optLambda, opt.all$optLambda))
opt.all$optLambda## [1] 1.401621edgeHeat(opt.all$optPrec, diag = F, textsize = 0.1)## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
CNplot(covML(data.numeric),
lambdaMin = 1e-11,
lambdaMax = 1000,
step = 5000,
Iaids = T,
vertical = T,
value = opt.all$optLambda)## Perform input checks...
## Calculating spectral condition numbers...
## Calculating interpretational aids...
## Plotting...
i = 0.1
edgeHeat(M = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 46139
## - Corresponding to 18.81 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.5
edgeHeat(M = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 46110
## - Corresponding to 18.79 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.9
edgeHeat(M = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 32121
## - Corresponding to 13.09 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 0.999
edgeHeat(M = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 18492
## - Corresponding to 7.54 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 1-1e-6
edgeHeat(M = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 11370
## - Corresponding to 4.63 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
i = 1-1e-10
edgeHeat(M = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
diag = F, textsize = 1,
main = paste("False Discovery Rate cutoff:", i))## - Retained elements: 7263
## - Corresponding to 2.96 % of possible edges
## ## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
Color mapping for different omics:
Smallest possible FDRcut:
P0.all.min = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=1-1e-14)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 5784
## - Corresponding to 2.36 % of possible edges
## PcorP = pruneMatrix(P0.all.min$sparseParCor)
Colors.min <- rownames(PcorP)
Colors.min[grep("hsa", rownames(PcorP))] <- "red"
Colors.min[grep(".RPPA", rownames(PcorP))] <- "green"
Colors.min[grep(".R2Gn", rownames(PcorP))] <- "cyan"#dev.new(width = 20, height = 20, unit = "in", noRStudioGD = F)
set.seed(42)
Ugraph(PcorP, type = "fancy", lay = "layout_with_fr",
Vcolor = Colors.min, Vsize = 2, Vcex = .3, prune = T, cut = 0.5,
main = "All Numerical data\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\nRed: miRNASeqGene; Blue: RNASeq2GeneNorm; Green: RPPA Array")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] 6.20561655 3.53495957
## [2,] 7.46396525 -0.77381923
## [3,] 7.09032175 6.23813463
## [4,] 0.44980686 -0.71576151
## [5,] -4.70019262 10.22128884
## [6,] -1.69553119 3.25018459
## [7,] -2.64191096 3.98895642
## [8,] -4.04638984 11.18501863
## [9,] -8.80321991 -0.93321937
## [10,] -0.68545340 3.51833639
## [11,] -5.04246353 5.15978586
## [12,] -4.15680078 4.73729267
## [13,] -2.87979060 4.27410950
## [14,] -4.95681191 4.43927019
## [15,] -1.55025292 3.31852244
## [16,] -3.70730696 2.73799578
## [17,] 2.92346834 4.32803403
## [18,] 0.11476041 1.27095399
## [19,] 5.18051584 9.17247255
## [20,] -0.97149384 3.71746115
## [21,] -1.65655542 2.69010492
## [22,] -0.75016099 4.02487543
## [23,] -1.32427219 3.74061979
## [24,] -0.46707460 4.63068239
## [25,] -2.22970578 6.59677246
## [26,] 1.44494776 2.14949872
## [27,] -0.44455543 3.43419864
## [28,] -3.59201386 6.28705967
## [29,] 0.86161343 4.00550641
## [30,] 1.52064344 2.97018628
## [31,] 6.12123851 4.16387269
## [32,] 4.32099628 5.28189663
## [33,] -2.78424857 2.59963354
## [34,] -6.74102748 -3.39571616
## [35,] -0.70437176 2.98939932
## [36,] -9.72616996 7.84655216
## [37,] -0.47334387 4.25685297
## [38,] -1.02886612 4.79243406
## [39,] 0.76019974 0.80779110
## [40,] -2.71461657 1.53766897
## [41,] -1.40784584 -3.50587806
## [42,] -2.25151573 4.34172313
## [43,] -1.53288446 4.04875446
## [44,] -0.33762429 -1.04753151
## [45,] 0.18858460 4.46202711
## [46,] -0.37527163 1.43662306
## [47,] -2.27722848 3.26684735
## [48,] -0.04136483 3.22492507
## [49,] -2.34062048 -2.17611840
## [50,] -2.21332439 3.73746074
## [51,] -6.09103371 7.75167631
## [52,] 0.71016522 3.37764081
## [53,] -1.14186944 3.58850216
## [54,] 0.45387535 1.59998812
## [55,] -8.67958568 7.34194436
## [56,] -1.10736393 3.43993741
## [57,] -0.92100147 4.50166466
## [58,] -0.14741028 5.49378117
## [59,] -4.01177196 1.30019931
## [60,] -1.79203421 -0.96592241
## [61,] 1.96749301 8.20237105
## [62,] -6.11672901 0.41633904
## [63,] 8.25274443 1.11661797
## [64,] -8.15952840 1.13732458
## [65,] 0.84800368 -3.12260948
## [66,] -4.69365442 -1.94822063
## [67,] -0.81929430 3.51094873
## [68,] 0.16582338 4.28885181
## [69,] -5.66803404 7.45634578
## [70,] 3.29135542 6.54025057
## [71,] 4.17232889 7.40701842
## [72,] 3.77524161 6.24468401
## [73,] 0.64816384 6.83271701
## [74,] 1.35992752 5.08253069
## [75,] -0.05546907 3.46988660
## [76,] -1.20478188 2.77475443
## [77,] 0.82965158 11.46212178
## [78,] -0.80945978 2.18626332
## [79,] -1.92020438 3.49318649
## [80,] -2.92497916 -1.39592158
## [81,] 7.13704693 -1.19015440
## [82,] -5.45639431 -0.21282837
## [83,] -5.73172816 0.19168379
## [84,] -6.47416503 1.98061445
## [85,] -0.27757270 5.91410184
## [86,] 0.08391602 5.07119998
## [87,] 1.36971015 8.39508663
## [88,] 0.69276474 -1.51504462
## [89,] -1.13335456 -1.60402484
## [90,] -1.58819452 -1.54408671
## [91,] -0.45600533 2.21497976
## [92,] 0.28221268 2.30177437
## [93,] 1.84662476 6.11775540
## [94,] 1.27101054 1.15768912
## [95,] 1.48752823 3.72754132
## [96,] -2.11491601 4.69886612
## [97,] -1.21368886 3.99295432
## [98,] -3.71256434 11.64260968
## [99,] -2.21892849 0.81004706
## [100,] -0.67735206 1.84965454
## [101,] 1.33259548 -0.58351819
## [102,] 0.01784759 8.94833166
## [103,] 0.28782953 4.07208264
## [104,] -1.42034627 2.91075199
## [105,] 0.05537023 6.07065205
## [106,] -0.62928824 4.19030920
## [107,] -1.27972908 9.82121821
## [108,] -4.50077273 1.60140635
## [109,] 2.74955122 -4.70948408
## [110,] 4.39729356 -1.53832635
## [111,] 2.56254112 -1.17832362
## [112,] -0.26962669 0.56403246
## [113,] -3.89496138 -1.00521823
## [114,] -7.86685783 -2.39844328
## [115,] 1.22738117 4.84513175
## [116,] -0.36397592 3.35443251
## [117,] -7.39373042 6.38622879
## [118,] 1.91092406 -2.84625309
## [119,] 2.07654585 2.27034811
## [120,] -2.10709699 9.84152160
## [121,] 8.12804100 2.33295620
## [122,] -1.66603773 6.34373992
## [123,] -0.52473197 3.14476425
## [124,] -3.31712891 3.59800862
## [125,] -1.68487396 2.96610667
## [126,] 0.55021039 3.64030411
## [127,] -2.91485909 6.29250924
## [128,] -1.24447093 2.12825541
## [129,] -0.81354316 2.63284498
## [130,] -0.15815404 3.69109849
## [131,] 0.29257953 2.44257024
## [132,] -1.49354354 3.53231106
## [133,] -0.32755883 4.65494480
## [134,] -1.02953258 3.94697401
## [135,] -5.89855918 -0.91810076
## [136,] -1.48454464 1.51235982
## [137,] -1.76841174 3.65942250
## [138,] -6.53175790 5.82577306
## [139,] -6.68988019 5.31456457
## [140,] -2.92027793 -5.38247340
## [141,] -4.87503088 5.06762417
## [142,] -3.52520912 5.12297931
## [143,] 1.96331166 -4.92802211
## [144,] 2.22022445 0.13738210
## [145,] 3.96200014 -3.44267559
## [146,] 4.07624974 -0.70359886
## [147,] 3.25245646 10.89986377
## [148,] -0.63492513 4.36275168
## [149,] -5.06756978 -4.35156183
## [150,] -1.12057451 3.14822853
## [151,] -2.03905255 0.81878143
## [152,] 7.65706014 0.79983728
## [153,] -0.11971404 4.62508311
## [154,] 0.99751454 3.84464132
## [155,] -1.95827056 3.23656576
## [156,] 0.02069920 -4.73951677
## [157,] -0.19124571 1.61117144
## [158,] -2.75561679 11.95290923
## [159,] 2.55843478 11.17143487
## [160,] -1.18816672 6.60500034
## [161,] 6.88889153 -1.68369652
## [162,] -3.48737316 2.38682102
## [163,] -0.67405184 4.99401868
## [164,] 0.52099160 3.33873255
## [165,] -0.87176412 3.21692198
## [166,] -1.02275956 1.49541339
## [167,] 4.95275986 6.83440732
## [168,] -0.46107902 1.68856694
## [169,] -1.50130335 3.89449675
## [170,] -1.78068450 4.55133529
## [171,] -1.08104114 3.77046568
## [172,] -7.47547085 6.80962106
## [173,] -0.51648103 3.75850951
## [174,] -2.43808599 7.29156802
## [175,] -4.64256281 5.93382600
## [176,] -3.98890057 5.77674920
## [177,] -5.74821097 6.78257838
## [178,] -8.88539196 6.06565213
## [179,] -7.14895947 7.36902307
## [180,] -8.26745356 7.57307701
## [181,] -3.79006129 4.47767523
## [182,] -9.57019364 5.39507434
## [183,] -7.94516592 5.99942927
## [184,] 0.63965551 3.00609291
## [185,] 1.45836826 4.19857026
## [186,] -5.77838508 3.71493154
## [187,] 0.86679640 3.61779292
## [188,] 0.28620957 1.42585685
## [189,] -0.30089294 2.27894193
## [190,] -0.58394334 5.25250982
## [191,] -2.05680690 3.05772390
## [192,] -0.74836461 0.87331356
## [193,] -1.04770126 3.29799509
## [194,] -1.38754245 4.49881412
## [195,] -8.07797408 5.67146411
## [196,] -9.10710607 6.71788492
## [197,] -4.86992509 4.84669090
## [198,] -2.98942007 7.21140005
## [199,] -0.12842693 3.08713744
## [200,] -9.05879044 4.84162142
## [201,] -7.63813416 4.98524203
## [202,] 0.62309959 5.70389132
## [203,] -4.56756301 3.98431629
## [204,] 1.76789899 7.03393683
## [205,] 2.16852000 1.14831424
## [206,] 4.37514582 0.84933548
## [207,] -0.00686493 2.30267431
## [208,] -0.08301591 -0.22895130
## [209,] -6.10776391 5.60590145
## [210,] 2.68244847 6.57161777
## [211,] -5.32434117 5.36909945
## [212,] -8.46768648 5.32352573
## [213,] -1.03057404 1.98573349
## [214,] -0.46394987 2.86575064
## [215,] -1.80564666 2.91432837
## [216,] -3.29854269 11.42820994
## [217,] -6.01720742 6.73101284
## [218,] -6.52262908 6.59255872
## [219,] -7.26558925 5.85431101
## [220,] -6.10131551 1.00718786
## [221,] -0.32395253 2.98993790
## [222,] 4.54544200 -4.08831537
## [223,] -0.14236999 2.98885522
## [224,] 4.41351756 2.30248268
## [225,] 3.12004280 2.54982948
## [226,] 4.83282086 2.28120912
## [227,] 4.37508421 3.40924147
## [228,] 4.98494167 3.32432965
## [229,] 4.40479794 3.13689408
## [230,] 2.32997633 2.95252405
## [231,] -2.68327495 4.96229721
## [232,] -2.78332094 2.93622473
## [233,] 4.80429790 2.06169208
## [234,] 3.03770493 3.14275145
## [235,] 4.12523074 2.89486942
## [236,] 3.99211407 2.71085390
## [237,] 4.90576048 2.87143913
## [238,] 4.75665392 2.59693051
## [239,] 5.09489225 2.53111317
## [240,] 2.90290309 2.24706154
## [241,] -1.39056058 5.27036469
## [242,] -2.39333693 4.83307841
## [243,] -1.78007027 5.44595654
## [244,] -2.04067794 4.47520263
## [245,] -2.44908005 3.77798543
## [246,] -1.72440317 3.98964368
## [247,] -2.69972813 3.44874267
## [248,] -1.89773925 4.16021642
## [249,] -2.30312565 4.04402584
## [250,] -1.66864414 4.91588128
## [251,] -2.23812383 5.23486172
## [252,] -2.42246868 3.39355419
## [253,] -8.11629188 8.00193357
## [254,] -4.18698912 4.37672528
## [255,] -6.96364312 4.36592353
## [256,] -3.10117633 2.21506322
## [257,] -1.38133268 2.44111771
## [258,] -2.07222094 2.90083341
## [259,] 0.20942769 3.72371878
## [260,] 0.42267309 2.56949342
## [261,] 0.68542755 0.57104519
## [262,] 0.32303029 7.18250415
## [263,] 0.31399662 4.28757958
## [264,] -0.23429558 1.96143948
## [265,] -0.97963475 0.59609182
## [266,] -1.67032858 4.72927220
## [267,] -1.07203315 3.00440947
## [268,] -1.85946260 3.72898413
## [269,] -2.12940278 2.65169819
## [270,] -0.85829644 11.48930842
## [271,] 3.64627642 0.80176243
## [272,] 0.55892951 2.85850817
## [273,] -0.42248246 2.47514078
## [274,] -8.35398741 -1.35875741
## [275,] 0.81389733 2.95464074
## [276,] -1.85467744 5.19632064
## [277,] -1.01760059 5.14907153
## [278,] 7.94475226 1.63802249
## [279,] -1.58856148 2.60229414
## [280,] -1.27171356 7.81777573
## [281,] -3.32683692 0.92427556
## [282,] -2.80656328 1.89441055
## [283,] -0.56895976 2.40907603
## [284,] 3.15896755 3.85265613
## [285,] 4.37680292 -0.22288044
## [286,] 1.69401769 0.02003265
## [287,] -9.97130056 5.07490730
## [288,] -4.62275472 5.52954151
## [289,] -4.14367718 4.86283279
## [290,] 2.45290178 7.02223254
## [291,] -0.60374247 3.91470565
## [292,] -2.89995860 3.78073677
## [293,] -5.87847858 5.87245403
## [294,] -1.59324667 2.77996324
## [295,] 1.09239609 4.27343325
## [296,] 6.39339991 7.65771171
## [297,] -0.21934091 4.97111070
## [298,] -1.87781270 3.84751410
## [299,] -0.50266528 7.12800848
## [300,] -9.32555036 6.16933035
## [301,] 2.55110473 4.01127715
## [302,] -1.42405251 1.22084769
## [303,] -1.67332390 2.52529994
## [304,] -4.23481264 5.26258904
## [305,] -0.30793698 2.76331784
## [306,] -1.30070494 3.08582943
## [307,] 0.69487598 2.51061146
## [308,] -8.23802889 6.51520708
## [309,] 0.23591206 3.56078669
## [310,] -2.48410155 2.28279555
## [311,] -2.18111011 1.72028936
## [312,] -1.82942341 3.15321659
## [313,] -4.61445378 -0.96070591
## [314,] 0.10632790 4.88335179
## [315,] 0.64893666 5.30016925
## [316,] -2.35426115 4.57216559
## [317,] -2.33016615 -0.02105687
## [318,] 2.78645361 4.95745157
## [319,] 0.80979082 5.78119040
## [320,] -4.65047428 11.14237649
## [321,] 6.08945968 8.44513806
## [322,] 0.05956105 5.48266403
## [323,] -5.99730207 9.77777280
## [324,] -3.84385390 -5.23663395
## [325,] -1.20943724 1.68834238
## [326,] -1.39308904 6.69810330
## [327,] -2.39639507 2.11093029
## [328,] 0.71953099 -2.53750307
## [329,] 2.57131357 5.36262313
## [330,] -0.31198605 2.60744835
## [331,] -2.00511738 11.48052628
## [332,] 1.21210493 2.40284763
## [333,] -2.54334343 11.62641056
## [334,] 3.46626655 1.75273838
## [335,] 0.13190299 0.35810466
## [336,] 1.75877315 3.86709268
## [337,] -0.63730535 2.06664375
## [338,] 2.92051697 8.75314267
## [339,] -1.64100901 11.99736961
## [340,] 1.77019087 -1.75221361
## [341,] 3.23339752 -3.88754007
## [342,] -0.88451760 2.93985089
## [343,] 8.22042920 2.93564407
## [344,] -1.81016118 1.60571115
## [345,] -1.70358983 1.30340325
## [346,] -4.51590038 -4.55114956
## [347,] 6.77430135 7.02516237
## [348,] 0.54516226 2.17926622
## [349,] -0.60504632 8.28798181
## [350,] -0.89214700 -3.59384484
## [351,] -0.03332699 0.94730345
## [352,] 1.85872448 -1.21349062
## [353,] 2.13936616 6.90786003
## [354,] -1.89483400 2.13065897
## [355,] -4.31727250 -2.37203621
## [356,] -0.36165657 -1.84614881
## [357,] -1.40116424 2.30852471
## [358,] -0.18550006 3.85248935
## [359,] -0.33201224 3.54838705
## [360,] 1.33776450 1.86143279
## [361,] -0.06619153 9.91842326
## [362,] 7.99662562 0.28101230
## [363,] -4.23244482 -4.91018121
## [364,] -7.82481191 0.30721112
## [365,] -1.51151126 4.25331434
## [366,] -4.96156720 0.87470597
## [367,] -3.01617025 9.43661410
## [368,] -1.09208771 4.45073566
## [369,] -2.19932413 -5.40219844
## [370,] -4.48704678 0.08329512
## [371,] -1.84835264 2.36653659
## [372,] -3.30131600 7.03549437
## [373,] 0.91824536 1.98722453
## [374,] -2.58685199 0.51392047
## [375,] -3.18426135 3.25772822
## [376,] 0.68142328 1.39066331
## [377,] 5.64650382 4.93368254
## [378,] -0.04078212 -2.90411068
## [379,] 0.10624134 11.76048715
## [380,] -7.25368594 -3.00435347
## [381,] 2.68194363 5.80955822
## [382,] -0.93519288 4.14828233
## [383,] -0.39328304 -3.59359121
## [384,] 2.15193313 4.63930050
## [385,] 6.43825006 -2.17160348
## [386,] 3.72537705 3.87980276
## [387,] -5.07890901 2.68169578
## [388,] -8.07444656 -1.85921244
## [389,] -1.32498743 0.55494688
## [390,] -1.69525728 -6.31905719
## [391,] 7.72487303 -0.30683679
## [392,] -2.95401950 -3.29902366
## [393,] 0.37660040 2.88837231
## [394,] 3.91147731 -1.93426288
## [395,] -0.02146038 2.79282044
## [396,] -0.73827285 6.20082507
## [397,] -1.56594955 -0.04576967
## [398,] 0.37749086 0.65886951
## [399,] -1.36951151 1.44099775
## [400,] 3.05082991 7.17133393
## [401,] 4.67518205 9.15470102
## [402,] -6.40551642 2.80191459
## [403,] -1.91116751 8.59599960
## [404,] -1.29466983 2.62424564
## [405,] 3.60078611 7.96096064
## [406,] 0.59397540 8.62179331
## [407,] -2.07140509 2.11509594
## [408,] 4.87159108 1.49362716
## [409,] 4.82469197 0.18470023
## [410,] 4.23844402 5.83645365
## [411,] 0.33046295 3.03360648
## [412,] 2.59031249 0.63191672
## [413,] -0.90346526 2.38764242
## [414,] -0.31049383 0.88737480
## [415,] 0.65904252 1.95356224
## [416,] 0.61575787 3.90702182
## [417,] 0.23519819 1.95645750
## [418,] -0.09040095 4.44712331
## [419,] 1.62094376 1.46325598
## [420,] -0.56941003 1.30065917
## [421,] 0.49596715 4.02943567
## [422,] -2.99074837 0.12210378
## [423,] -0.24763669 6.30853611
## [424,] -7.42294030 -2.52197499
## [425,] -5.30367942 9.98833039
## [426,] -3.29289825 -5.03287788
## [427,] -4.96571016 3.15154371
## [428,] 8.15368512 3.63088290
## [429,] 2.79393831 0.96075594
## [430,] -3.51575244 0.35444461
## [431,] 3.81983104 10.40346165
## [432,] 0.75751663 9.70151101
## [433,] -6.33708113 1.61152048
## [434,] 1.43995254 7.60748016
## [435,] 5.29342087 6.02388355
## [436,] -0.68369244 2.43507974
## [437,] -2.40195711 2.03249615
## [438,] -0.82302575 4.53879504
## [439,] 0.19154348 4.63179146
## [440,] 0.41728056 3.39246980
## [441,] -0.08647039 2.44156408
## [442,] 1.17930327 2.91902559
## [443,] -1.07675347 2.47837536
## [444,] -1.18709554 3.08823029
## [445,] 1.40511284 6.01783596
## [446,] -1.07408906 2.25362674
## [447,] -1.36054138 4.10717284
## [448,] 0.44031066 4.95328368
## [449,] 3.12935649 5.16850366
## [450,] 0.85857824 4.83202108
## [451,] -0.60857871 2.69217744
## [452,] 0.15009231 3.89576006
## [453,] -1.34191076 4.54161755
## [454,] 1.78866983 4.26833853
## [455,] -1.00054721 8.77091947
## [456,] -1.02547450 1.17528365
## [457,] 0.97855207 3.06567513
## [458,] 0.21323945 2.76290086
## [459,] 1.08442612 4.52403479
## [460,] 2.02878208 3.51025782
## [461,] 0.02901680 1.81891696
## [462,] -0.22747103 4.01884055
## [463,] 0.29185595 3.83467755
## [464,] -0.80720618 1.54024221
## [465,] 1.94258479 2.90714312
## [466,] -0.10612897 3.32628155
## [467,] -1.25094261 4.84707182
## [468,] 1.09555320 3.27590602
## [469,] -1.63532742 2.00002325#Ugraph(P0.all.min$sparseParCor, type = "fancy", lay = "layout_in_circle",
# Vcolor = Colors, Vsize = 2, Vcex = .1, prune = T, cut = 0.5,
# main = "All Numerical data\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\nRed: miRNASeqGene; Blue: RNASeq2GeneNorm; Green: RPPA Array")GGM.all.min = as.data.frame(GGMnetworkStats(P0.all.min$sparseParCor, as.table = T))
GGM.all.min.order = GGM.all.min[order(GGM.all.min$degree, decreasing = T), ]
#Output top 5%
GGM.all.min.order[1:round(nrow(GGM.all.min.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos
## TFRC.RPPA 169 10373.5624 0.001277139 0.9850321 85 84
## G6PD.RPPA 159 9892.9879 0.001267427 0.9602916 82 77
## FN1.R2Gn 152 8133.2726 0.001206273 0.8971745 75 77
## PDCD4.RPPA 152 4317.4734 0.001215067 1.0000000 79 73
## GATA3.RPPA 149 7265.6529 0.001209190 0.8861581 82 67
## MYH11.RPPA 147 6234.8141 0.001236094 0.9386265 78 69
## GAPDH.R2Gn 145 7946.8327 0.001236094 0.8858551 90 55
## FASN.RPPA 139 4727.9854 0.001179245 0.8717441 67 72
## GAPDH.RPPA 136 6960.5542 0.001215067 0.8157612 76 60
## IGFBP2.RPPA 134 4194.5097 0.001219512 0.9011127 71 63
## HSPA1A.R2Gn 123 4596.7037 0.001175088 0.7832807 60 63
## SQSTM1.R2Gn 122 2717.4029 0.001187648 0.8582847 66 56
## ATM.RPPA 120 3611.8223 0.001183432 0.8328589 52 68
## EEF2.R2Gn 119 3132.9558 0.001201923 0.8634166 66 53
## SYP.R2Gn 108 2689.7774 0.001157407 0.7504667 62 46
## RPS6.R2Gn 92 2431.7576 0.001154734 0.6364864 50 42
## TGM2.R2Gn 86 1098.2465 0.001136364 0.6710201 45 41
## hsa-mir-206 80 784.1257 0.001090513 0.6025077 38 42
## PTEN.RPPA 79 1765.0714 0.001107420 0.5871044 44 35
## hsa-mir-135a-2 75 593.4168 0.001090513 0.5701744 39 36
## SERPINE1.R2Gn 75 615.6018 0.001095290 0.5987406 39 36
## MSH6.RPPA 74 1229.5074 0.001135074 0.6293040 40 34
## CTNNB1.R2Gn 73 1330.8679 0.001127396 0.5619557 37 36
## hsa-mir-137 72 500.2896 0.001088139 0.5893915 42 30
## hsa-mir-383 72 1269.5811 0.001097695 0.5421469 40 32
## hsa-mir-122 71 891.7021 0.001057082 0.5096415 42 29
## hsa-mir-577 71 605.3406 0.001113586 0.5741710 36 35
## RBM15.RPPA 67 635.4458 0.001068376 0.5442953 35 32
## EGFR.RPPA 67 531.9742 0.001081081 0.5499040 38 29
## TTF1.RPPA 67 1195.4745 0.001090513 0.4829827 37 30
## hsa-mir-216b 62 444.1900 0.001042753 0.5044072 35 27
## COL6A1.R2Gn 62 456.3206 0.001086957 0.5241064 32 30
## hsa-mir-329-1 61 3491.2505 0.001052632 0.2702204 16 45
## hsa-mir-34b 61 282.2201 0.001075269 0.5389143 35 26
## IGFBP2.R2Gn 61 396.6226 0.001064963 0.5108260 35 26
## mutualInfo variance partialVar
## TFRC.RPPA 0.25183992 1.286390 1
## G6PD.RPPA 0.18495319 1.203162 1
## FN1.R2Gn 0.16828121 1.183269 1
## PDCD4.RPPA 0.18488663 1.203082 1
## GATA3.RPPA 0.17643277 1.192954 1
## MYH11.RPPA 0.21977181 1.245792 1
## GAPDH.R2Gn 0.17838389 1.195284 1
## FASN.RPPA 0.13972037 1.149952 1
## GAPDH.RPPA 0.23505358 1.264977 1
## IGFBP2.RPPA 0.15340686 1.165799 1
## HSPA1A.R2Gn 0.12917152 1.137885 1
## SQSTM1.R2Gn 0.12038576 1.127932 1
## ATM.RPPA 0.13056338 1.139470 1
## EEF2.R2Gn 0.13322568 1.142508 1
## SYP.R2Gn 0.09357269 1.098090 1
## RPS6.R2Gn 0.08136276 1.084764 1
## TGM2.R2Gn 0.06537766 1.067562 1
## hsa-mir-206 0.05378838 1.055261 1
## PTEN.RPPA 0.05664719 1.058282 1
## hsa-mir-135a-2 0.05338992 1.054841 1
## SERPINE1.R2Gn 0.05071932 1.052028 1
## MSH6.RPPA 0.06011756 1.061961 1
## CTNNB1.R2Gn 0.05987052 1.061699 1
## hsa-mir-137 0.04712975 1.048258 1
## hsa-mir-383 0.05167955 1.053038 1
## hsa-mir-122 0.04655721 1.047658 1
## hsa-mir-577 0.03825078 1.038992 1
## RBM15.RPPA 0.04954582 1.050794 1
## EGFR.RPPA 0.04636673 1.047458 1
## TTF1.RPPA 0.04556085 1.046615 1
## hsa-mir-216b 0.04401936 1.045003 1
## COL6A1.R2Gn 0.04746232 1.048607 1
## hsa-mir-329-1 0.02783208 1.028223 1
## hsa-mir-34b 0.03341964 1.033984 1
## IGFBP2.R2Gn 0.06572742 1.067936 1Colors.min.plot <- rownames(GGM.all.min.order)
Colors.min.plot[grep("hsa", rownames(GGM.all.min.order))] <- "miRNASeqGene"
Colors.min.plot[grep(".RPPA", rownames(GGM.all.min.order))] <- "RPPA Array"
Colors.min.plot[grep(".R2Gn", rownames(GGM.all.min.order))] <- "RNASeq2GeneNorm"ggplot(GGM.all.min.order, aes(x = reorder(rownames(GGM.all.min.order), -degree), y = degree, color = Colors.min.plot)) +
geom_point() +
geom_hline(yintercept = mean(GGM.all.min.order$degree), linetype = "dashed", color = "red") +
# 36th unit: top 5%
geom_hline(yintercept = GGM.all.min.order[36,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "All Data", guide = guide_axis(angle = 90)) +
scale_color_manual(values = c("red", "blue", "green3"))+
ggtitle("Variables sorted by degree, FDR = 1-1e-14")
FDRcut 1-1e-6:
P0.all.6 = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=1-1e-6)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 11370
## - Corresponding to 4.63 % of possible edges
## PcorP.6 = pruneMatrix(P0.all.6$sparseParCor)
Colors.6 <- rownames(PcorP.6)
Colors.6[grep("hsa", rownames(PcorP.6))] <- "red"
Colors.6[grep(".RPPA", rownames(PcorP.6))] <- "green"
Colors.6[grep(".R2Gn", rownames(PcorP.6))] <- "cyan"#dev.new(width = 20, height = 20, unit = "in", noRStudioGD = F)
set.seed(42)
Ugraph(PcorP.6, type = "fancy", lay = "layout_with_fr",
Vcolor = Colors.6, Vsize = 2, Vcex = .3, prune = T, cut = 0.5,
main = "All Numerical data\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\nRed: miRNASeqGene; Blue: RNASeq2GeneNorm; Green: RPPA Array")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] -1.60759887 -6.971002543
## [2,] -0.63663163 3.406811549
## [3,] 9.65375258 3.702930524
## [4,] 9.10446249 -0.011686856
## [5,] 9.79885530 7.024422253
## [6,] -5.87970713 3.807967700
## [7,] 3.83399865 -3.805636798
## [8,] 1.42817854 -1.897861225
## [9,] 9.44455630 7.452278140
## [10,] 2.21521272 1.071936169
## [11,] 3.21657003 1.553532256
## [12,] 10.00105487 0.123812260
## [13,] 3.13631927 -7.908435787
## [14,] -0.60331983 6.050929837
## [15,] 2.90717255 0.904118360
## [16,] 4.39441511 3.302756223
## [17,] 4.53911660 3.183406504
## [18,] 4.50487045 2.856305398
## [19,] 4.14870195 2.988487473
## [20,] 2.80412156 0.564845063
## [21,] 4.65702236 -0.607470487
## [22,] 0.14921829 1.593027528
## [23,] 2.20221006 2.477653252
## [24,] -0.99358917 1.185969190
## [25,] 3.45328558 0.461906670
## [26,] 3.32356930 0.812676144
## [27,] 2.94207072 -0.004678972
## [28,] 3.00265127 1.412332018
## [29,] 3.78418141 0.947696248
## [30,] 5.16723410 1.427714147
## [31,] 1.41965427 2.419024142
## [32,] 3.68534379 1.330132944
## [33,] 3.08994322 3.685021838
## [34,] 2.81001031 -0.320208247
## [35,] 1.82260488 0.127538867
## [36,] -0.78964152 0.566770751
## [37,] 0.20328025 2.032256302
## [38,] 1.43659164 1.937828010
## [39,] 6.33046621 2.033043091
## [40,] 1.97653169 0.948642046
## [41,] 5.82921334 4.407123391
## [42,] 2.22102859 1.690463845
## [43,] 3.18413060 2.098229024
## [44,] 1.53187020 -0.589330033
## [45,] 4.64792697 0.963846724
## [46,] 12.16630141 1.849875188
## [47,] 3.43590952 -2.171149748
## [48,] 2.73059462 2.262297867
## [49,] 3.51395173 1.357800987
## [50,] -0.21676758 0.297461811
## [51,] 3.53132612 1.548096779
## [52,] 3.08259394 -0.302345494
## [53,] 3.65007983 -8.350629198
## [54,] 3.09762208 0.535146143
## [55,] 3.11725764 0.657009947
## [56,] -3.29674553 2.977827292
## [57,] 3.53641162 0.273707188
## [58,] 6.37607308 3.990660498
## [59,] 3.32848656 2.092907999
## [60,] 3.14493341 1.099299379
## [61,] 2.70671353 -0.071397769
## [62,] 4.80585467 4.931344584
## [63,] 3.26550179 0.673328951
## [64,] 2.52525051 2.140143958
## [65,] 3.24776762 2.392186404
## [66,] 0.89898101 0.790033551
## [67,] 3.66673568 -1.394558704
## [68,] -2.64261289 4.009837366
## [69,] 1.93970645 -2.465279646
## [70,] 2.07011231 -8.321980621
## [71,] 7.16641824 0.527015581
## [72,] 5.84028207 -1.854570639
## [73,] -3.14195608 1.736751438
## [74,] -1.18498995 -4.757464224
## [75,] 0.14565656 3.270983783
## [76,] 6.27286924 -3.100545865
## [77,] 7.27604411 -0.040546139
## [78,] 2.98594810 1.768944449
## [79,] 2.38088636 -0.126680669
## [80,] 5.58629528 4.398411909
## [81,] 1.83941319 5.044007665
## [82,] -2.39339924 -6.827511255
## [83,] -5.76604251 -2.356450845
## [84,] -4.81695199 -4.007367383
## [85,] 3.19548808 -2.956202450
## [86,] 1.95832365 -2.908362353
## [87,] 2.24422792 -1.531710487
## [88,] 2.69194415 -1.199167427
## [89,] 2.46097661 0.507824413
## [90,] 3.22369670 1.555493749
## [91,] 8.61713519 -2.066579175
## [92,] 4.52468846 -6.246089360
## [93,] 3.05919704 5.206068532
## [94,] 2.40690107 1.596689805
## [95,] 3.06584768 0.042861774
## [96,] -0.77867019 0.125268947
## [97,] 10.88328393 5.726342032
## [98,] 1.11174271 7.077559492
## [99,] -1.03189215 -1.119035545
## [100,] -1.14305288 -0.385196909
## [101,] -0.25221591 2.032464263
## [102,] 1.00391593 -0.372560178
## [103,] 1.90825752 0.333703950
## [104,] -0.40380582 1.788242317
## [105,] -0.34718507 -1.980829302
## [106,] -0.82061281 -1.294119225
## [107,] -1.42914381 -0.050385575
## [108,] 2.35011430 1.119444198
## [109,] 3.42494217 -0.007748603
## [110,] 8.11443800 -6.137747700
## [111,] 4.44707647 -1.514788840
## [112,] 2.64108579 -0.903531450
## [113,] 4.06096167 -0.569297806
## [114,] 4.01920439 1.083722908
## [115,] 3.59063609 1.208312716
## [116,] 11.64701518 3.122058570
## [117,] -6.21576085 3.204468540
## [118,] 4.37092835 -1.993102368
## [119,] 2.52368244 -0.291096098
## [120,] 0.48131310 -1.573011707
## [121,] 0.55061767 -2.038853410
## [122,] 2.63668149 1.567265450
## [123,] 2.14250208 0.806184950
## [124,] 1.07406679 2.101886515
## [125,] 2.52390297 0.269278270
## [126,] 4.38022811 -2.485257811
## [127,] 1.99535850 -2.043455734
## [128,] 10.84368167 -4.029261312
## [129,] -1.66092269 -2.909372494
## [130,] -1.66837111 -2.362266022
## [131,] -0.63584362 -4.081975101
## [132,] 4.81592924 1.540408067
## [133,] 0.12360468 -1.723852348
## [134,] -1.98339774 -4.131527688
## [135,] -2.21422659 8.811684986
## [136,] 5.17140901 0.882242535
## [137,] -0.85423057 9.518405893
## [138,] -0.27253626 9.658701366
## [139,] 2.31968761 1.808823218
## [140,] 5.92785782 4.903646091
## [141,] 10.12250099 6.602818642
## [142,] 4.15189861 7.555871306
## [143,] -1.64776871 5.677174846
## [144,] 0.21451089 2.822379056
## [145,] 0.30761654 -7.429147276
## [146,] 0.85928100 0.478139481
## [147,] 6.86256053 -0.861209290
## [148,] 11.35867353 4.504769834
## [149,] 1.57519954 -6.292139337
## [150,] 1.98020959 2.854833188
## [151,] -2.57785549 8.483977012
## [152,] 3.57178645 0.767412800
## [153,] -5.60014269 2.549998802
## [154,] 2.13473859 2.131105375
## [155,] 3.31603137 1.022865455
## [156,] 2.00145201 0.201733382
## [157,] 0.15750046 0.665685508
## [158,] 4.05058696 0.606017253
## [159,] 3.14088752 1.623236902
## [160,] 2.02700763 1.191882368
## [161,] 2.26294186 0.532892896
## [162,] 3.04894376 1.070769785
## [163,] 3.61299018 1.340214688
## [164,] 3.91445118 0.606868525
## [165,] -0.12578820 3.644311298
## [166,] 2.40557705 2.335336858
## [167,] 2.68943026 1.294806717
## [168,] 5.80170083 4.613069523
## [169,] 5.46184405 4.572626991
## [170,] 1.78267663 4.380329983
## [171,] 2.94440906 6.258592959
## [172,] 4.28125099 2.842965371
## [173,] 3.93378789 2.867950693
## [174,] 5.77144087 -0.461925223
## [175,] 9.89705647 -0.584084924
## [176,] -0.78304104 0.862195354
## [177,] 6.74132550 8.229988981
## [178,] 6.85385173 -0.223838321
## [179,] 7.28169167 3.865826715
## [180,] 7.15176275 -1.431744564
## [181,] -6.01415408 -0.592639900
## [182,] 2.06179315 2.041367558
## [183,] 7.99612246 -4.179317115
## [184,] 3.34625815 0.776335201
## [185,] 4.80711079 1.856183551
## [186,] -2.51607353 0.475062534
## [187,] 3.64692448 -0.242051259
## [188,] -4.39085903 -4.526782542
## [189,] 3.78056292 1.563399696
## [190,] -0.97507181 -7.314075748
## [191,] 2.83658607 1.647726307
## [192,] 8.78772059 -0.540014594
## [193,] 2.09220100 1.347670487
## [194,] 11.83826388 2.212702760
## [195,] -4.16370903 0.574784275
## [196,] 1.47277103 2.947211335
## [197,] -6.38133214 1.709599446
## [198,] -6.30204363 2.496385529
## [199,] 1.70080117 1.969480851
## [200,] 3.60293638 1.919071714
## [201,] 10.70596936 5.365342048
## [202,] 1.65423008 0.716213533
## [203,] 2.74216992 1.123377094
## [204,] 3.70681713 0.007123143
## [205,] -2.37022496 -1.934416188
## [206,] 3.26782516 -0.182521483
## [207,] 3.74948012 1.152995322
## [208,] 3.03887371 2.148773797
## [209,] 6.22599538 4.099412089
## [210,] 3.70872348 0.342690778
## [211,] 5.01362223 4.953308992
## [212,] 2.23024946 1.563719063
## [213,] 10.18678158 -4.809614952
## [214,] 2.73278525 3.254310522
## [215,] 5.10276406 3.107619386
## [216,] 4.74881650 3.043609725
## [217,] 5.72858225 3.686971516
## [218,] 6.25089932 4.280165273
## [219,] 4.14370699 4.153085387
## [220,] 6.88542244 -2.211188619
## [221,] 6.18641120 4.557085287
## [222,] 4.23923489 3.336818693
## [223,] 5.72360597 4.750676086
## [224,] 5.54680637 5.050011117
## [225,] 2.57336298 0.085290777
## [226,] 3.32509683 -0.546844302
## [227,] 6.87217671 1.297632818
## [228,] -0.39052397 -5.562472729
## [229,] 3.11536988 1.275533918
## [230,] 4.08390055 -0.239667011
## [231,] 2.33301999 1.402718049
## [232,] 2.01079865 2.390178496
## [233,] 2.08177534 0.911718516
## [234,] 1.45205801 -0.216247585
## [235,] 2.93378017 0.593285653
## [236,] 3.36436112 1.889352880
## [237,] 5.34795346 5.226672830
## [238,] 5.32599001 5.005474540
## [239,] 4.39107504 3.534211577
## [240,] 5.42724440 2.593821500
## [241,] 1.21309513 -5.657422366
## [242,] 2.46172962 0.154869468
## [243,] 5.95859884 3.917833190
## [244,] 5.17970267 5.452556821
## [245,] 1.54005285 0.998799302
## [246,] 4.51430310 3.011893849
## [247,] 0.59542979 5.670944031
## [248,] -0.23359830 -7.693176836
## [249,] 5.24096022 -5.515181257
## [250,] 3.65802795 -2.315466282
## [251,] 3.44926236 2.919892448
## [252,] 5.31191111 -1.091424528
## [253,] 2.14650900 0.282571662
## [254,] 1.17931121 1.092901698
## [255,] 0.90137544 -8.154920676
## [256,] 4.94476192 4.283836285
## [257,] 2.79860393 -2.388648283
## [258,] 4.82191943 3.781123495
## [259,] 5.19544822 4.773336718
## [260,] 3.37975260 1.437475923
## [261,] 3.02720381 1.584050519
## [262,] 3.26718649 0.585572536
## [263,] 1.14897126 9.427865427
## [264,] 6.24970343 2.550252391
## [265,] 4.50006512 4.118808420
## [266,] 4.71532690 5.167975150
## [267,] 5.91854957 5.180847728
## [268,] -1.40971052 1.747000306
## [269,] 1.73318678 0.658079947
## [270,] 4.79376149 -3.191169828
## [271,] 2.37913073 0.238343034
## [272,] 8.42213253 8.213851866
## [273,] -0.34792609 -1.133597069
## [274,] 0.67824868 -0.047179835
## [275,] 0.17700328 -1.079262932
## [276,] -0.33119546 -1.354501444
## [277,] -0.25038917 -0.999227129
## [278,] -0.62948159 -0.848484541
## [279,] 1.07664573 -0.137610465
## [280,] 3.37617687 -1.191298521
## [281,] 3.78932235 -0.751742808
## [282,] 0.50365692 -0.581684398
## [283,] 1.08181660 0.022256079
## [284,] -0.09275696 -1.385956057
## [285,] -0.50385920 -0.461974299
## [286,] -0.16434816 -0.353022159
## [287,] 0.21265366 -0.788931567
## [288,] 0.10824107 -0.484362274
## [289,] 0.32889140 -0.154906341
## [290,] 4.35277892 0.790338300
## [291,] 4.28863422 0.448360055
## [292,] 4.69847606 0.392558941
## [293,] 4.14772262 0.993989825
## [294,] 4.23907030 1.216977195
## [295,] 3.54447772 0.904702045
## [296,] 4.47672637 1.144793822
## [297,] 3.19812795 1.286406253
## [298,] 4.11740998 0.419182769
## [299,] 4.14843713 1.547213529
## [300,] 4.35990940 1.667056591
## [301,] 3.88535221 1.433423869
## [302,] 5.44690115 4.845937004
## [303,] 4.43236229 2.587229701
## [304,] 10.46031970 -4.293028472
## [305,] 4.48414996 4.883506976
## [306,] 4.06304358 2.031095098
## [307,] 8.57469059 -5.120668516
## [308,] 2.61505983 1.187067460
## [309,] 3.39748285 1.061690159
## [310,] 2.85690365 1.914340571
## [311,] 3.10270408 0.284632691
## [312,] 1.64747790 2.164673418
## [313,] 5.10064474 0.613627610
## [314,] 2.79219830 1.029800840
## [315,] 2.59832323 1.794553663
## [316,] 4.62732591 0.062574190
## [317,] 3.97020133 0.175899147
## [318,] 2.65847578 0.530506576
## [319,] 3.42650102 1.716423331
## [320,] 2.98216494 0.319377425
## [321,] 6.40162541 -0.469811392
## [322,] 4.91616459 -1.145046078
## [323,] -3.83274119 -1.350961186
## [324,] 3.24566232 0.416044604
## [325,] 2.33931055 0.467438654
## [326,] -1.75676448 9.058894095
## [327,] 3.79545083 0.526947095
## [328,] 3.69879426 1.844032237
## [329,] 1.61526483 1.572735756
## [330,] -3.68356004 -5.556471026
## [331,] 2.86943382 1.360274445
## [332,] 2.00991149 -1.353840679
## [333,] 7.35097143 2.406272323
## [334,] 1.73844842 3.973436155
## [335,] 11.93276463 2.725502287
## [336,] -2.87530906 2.488163070
## [337,] 2.90175833 2.462854468
## [338,] 2.71956237 1.716346850
## [339,] 2.99886029 -1.146489874
## [340,] 5.03501708 0.231856985
## [341,] -1.28295241 9.223527002
## [342,] 11.64948641 4.052729056
## [343,] 4.82733617 -0.448599523
## [344,] 5.63256914 5.398722030
## [345,] 4.76748409 3.480189516
## [346,] 4.20809996 2.646189413
## [347,] 2.55518404 -2.284958501
## [348,] 2.98465081 1.367542095
## [349,] 4.04585877 2.770581122
## [350,] 4.54102820 3.283556409
## [351,] 3.43623724 0.714549254
## [352,] 9.88646938 -5.384330007
## [353,] 3.96589816 0.886167794
## [354,] 9.21164390 -1.409362364
## [355,] 1.88924447 1.609804872
## [356,] 2.44481772 1.915876167
## [357,] 4.96384812 -0.227377663
## [358,] 6.32679781 4.692367265
## [359,] 2.83921255 -0.955692397
## [360,] 2.68006568 -0.564473289
## [361,] 8.27561861 -3.509548128
## [362,] 2.51491032 1.030282845
## [363,] 4.10879561 3.193596026
## [364,] 2.34238158 1.536769912
## [365,] 4.24068043 6.829082748
## [366,] 2.00621033 1.823149840
## [367,] 2.71023369 -8.398091694
## [368,] 1.61051086 1.152280386
## [369,] -1.40670191 -0.680494871
## [370,] 4.75983445 4.603872369
## [371,] 3.05023062 0.796877476
## [372,] 4.34301991 0.867825964
## [373,] 4.43474842 0.633649518
## [374,] 3.36238183 1.502668895
## [375,] -0.26522512 2.753676893
## [376,] 2.86994689 2.719825808
## [377,] 1.26727575 1.386449192
## [378,] 4.00456201 -0.045281168
## [379,] 0.74213861 0.488705023
## [380,] 0.01131677 1.193447938
## [381,] 3.17770381 -1.581232730
## [382,] 7.53627222 4.020642028
## [383,] -1.06759317 2.607211809
## [384,] 5.00603938 -8.644613311
## [385,] 3.00833922 2.013411181
## [386,] 5.71869705 -3.003244951
## [387,] -3.47107461 7.830038383
## [388,] 4.45826799 -2.927623598
## [389,] -2.64034770 5.215647689
## [390,] 3.87225618 0.023909989
## [391,] 1.34918474 -0.992054952
## [392,] 1.72153213 -0.747526640
## [393,] 0.24190840 -2.887765155
## [394,] 4.29819107 -1.098005042
## [395,] -4.29917248 0.099263345
## [396,] 2.81806259 0.140937233
## [397,] 2.53240862 4.637211375
## [398,] 1.29099675 0.896291982
## [399,] 11.72417081 3.560308697
## [400,] -1.95965812 2.076230757
## [401,] 2.21152984 3.099899014
## [402,] 2.82387813 -1.593613658
## [403,] -1.18342918 -3.328425057
## [404,] 3.50933479 -0.528149643
## [405,] 2.56201447 0.580993323
## [406,] 11.21149378 -2.839028931
## [407,] 2.48575998 -4.751314220
## [408,] -5.13268642 -3.350014848
## [409,] 8.02532190 0.677793595
## [410,] 2.57654577 -1.851418077
## [411,] 7.03410471 -4.553260748
## [412,] -6.00708963 -1.618816188
## [413,] 3.55416501 0.156054502
## [414,] 2.53693952 -4.319337456
## [415,] 1.63028677 0.053836479
## [416,] 4.84224922 -3.783934792
## [417,] 5.53849754 -8.477019116
## [418,] 1.21483164 0.395941598
## [419,] -3.00131848 8.215245128
## [420,] 8.95097201 3.185665617
## [421,] 3.20648022 -3.482548676
## [422,] 5.97677598 2.562705184
## [423,] -3.37520360 -6.659352429
## [424,] -4.28397582 1.431428905
## [425,] 8.89927737 1.676213645
## [426,] 1.82631520 1.337626938
## [427,] 0.12329441 -3.529648217
## [428,] 7.95947863 -0.133402847
## [429,] 2.40625912 2.567196106
## [430,] 0.16931796 0.071640285
## [431,] 0.81569624 -2.299489707
## [432,] 1.91114139 1.004275502
## [433,] 7.78213558 -4.761420831
## [434,] -0.18434829 4.242993804
## [435,] 3.51371232 -3.807061527
## [436,] 3.24267696 1.878592838
## [437,] 2.39333243 0.308103592
## [438,] 2.69730313 0.252698950
## [439,] 2.20697277 -0.837766280
## [440,] 2.00865197 6.402004076
## [441,] 0.11047239 0.876840605
## [442,] -6.52034077 1.008528847
## [443,] -3.52796767 3.572825749
## [444,] 10.99292015 -3.454291992
## [445,] -0.63895054 -5.043965053
## [446,] 8.46943482 2.062010459
## [447,] 7.41589571 -3.087819177
## [448,] -2.96440645 4.282023273
## [449,] 3.19966879 -0.052278314
## [450,] 5.89097364 0.506223068
## [451,] 5.00154326 7.049531624
## [452,] 2.21961574 0.859010101
## [453,] 7.36736767 -7.996505637
## [454,] 2.21334285 3.831594896
## [455,] 6.51608811 -5.696435751
## [456,] 1.88007654 1.663337632
## [457,] 0.17061773 2.371750801
## [458,] 3.21522768 0.123725389
## [459,] 1.94987525 -1.037320563
## [460,] 1.05473666 2.344319363
## [461,] 3.13906268 -0.597363896
## [462,] 11.94800586 1.249132209
## [463,] -0.45003485 -3.215120654
## [464,] 6.45236560 -0.940372647
## [465,] -1.15645151 3.313606895
## [466,] 1.84827255 -5.704294214
## [467,] 0.49060778 -5.563478615
## [468,] 2.50180924 5.475068310
## [469,] 1.11294509 -1.001540667
## [470,] 2.79689925 1.485835905
## [471,] 7.74930558 -2.414443618
## [472,] 5.82574786 -5.679164241
## [473,] 1.78410850 3.074558666
## [474,] 2.12831162 -3.664003322
## [475,] 1.11163281 -1.811819208
## [476,] 0.73696847 2.932905946
## [477,] 8.83525367 -6.773385827
## [478,] 7.19712719 -2.216741020
## [479,] -5.58915131 4.544936249
## [480,] 0.36641380 6.167213377
## [481,] 4.18454051 -5.600168969
## [482,] 2.71915678 1.911211910
## [483,] -3.99086309 5.700082491
## [484,] 6.53159828 1.271217046
## [485,] -2.63344311 1.434275721
## [486,] 2.71313185 3.476993115
## [487,] 2.16388675 -0.258963978
## [488,] 0.63716391 -2.657828637
## [489,] 3.30326311 0.228357409
## [490,] 8.31332413 -7.341471378
## [491,] 4.68328847 -0.233324001
## [492,] 10.43867359 6.182118215
## [493,] 4.41518757 -0.688893286
## [494,] 0.93776906 1.502237655
## [495,] 3.66644066 -1.096563657
## [496,] 1.17895590 -2.619850107
## [497,] 2.62653033 -3.491844072
## [498,] 0.44669214 5.086205313
## [499,] 1.07687701 3.260556865
## [500,] 2.00300023 0.694608487
## [501,] -1.78236233 -0.504101894
## [502,] 1.21727935 -3.683260704
## [503,] 6.62610082 -2.694516221
## [504,] -3.04745697 -6.095929537
## [505,] 4.05597729 0.754674797
## [506,] -2.02969888 0.195336280
## [507,] 2.11857552 -3.302833542
## [508,] 3.76084889 -6.133831788
## [509,] 1.02592965 3.945383503
## [510,] 1.86974724 0.603155593
## [511,] 4.83628413 -1.975307823
## [512,] 6.91810272 -6.600498870
## [513,] 3.35773359 0.350546241
## [514,] -2.88913036 -2.923125084
## [515,] 2.95517655 -6.060382165
## [516,] 1.05652555 1.610127877
## [517,] 1.77000348 -0.327272909
## [518,] 2.11615826 0.095423130
## [519,] 6.90437225 -7.917931515
## [520,] -0.74908184 -2.184603574
## [521,] 6.01559962 -8.304209720
## [522,] -5.09160180 3.593976375
## [523,] 3.78918154 1.685740922
## [524,] 1.74370263 0.455180364
## [525,] 0.74137863 1.667018791
## [526,] 1.49708214 -8.301548041
## [527,] 4.21754996 0.298228167
## [528,] 1.96732056 1.147807687
## [529,] 3.55031673 -1.693043312
## [530,] 1.65645290 2.546138997
## [531,] 6.50641452 -8.258274233
## [532,] 6.92413526 -5.385604187
## [533,] 2.84860303 7.062358771
## [534,] 0.52623298 3.724470131
## [535,] 5.24524835 -6.067569696
## [536,] 3.02046531 -2.132725991
## [537,] 3.13785489 -6.535707553
## [538,] -0.11148895 -0.733192313
## [539,] 8.99250557 7.745850049
## [540,] 1.39983369 3.713671016
## [541,] 0.56802198 -4.195404288
## [542,] 4.44698978 5.911304010
## [543,] -0.92204545 1.784282491
## [544,] 7.71054043 -2.709107943
## [545,] 7.68481025 -7.512589103
## [546,] 3.79147634 -1.952686696
## [547,] 0.71402317 -1.176184121
## [548,] 1.93759990 -0.052098310
## [549,] 7.34235730 -5.108250342
## [550,] 1.75504905 2.359294202
## [551,] 2.89157093 -0.089567419
## [552,] 2.71166168 0.410546464
## [553,] 2.49052976 1.186647061
## [554,] 1.58935541 0.255574363
## [555,] 2.14886733 -0.459854990
## [556,] 2.18161790 0.571441079
## [557,] 2.57445028 0.821699130
## [558,] 1.82283446 -1.263700536
## [559,] 2.33521847 0.960257037
## [560,] 4.26227302 1.827358285
## [561,] 2.06770111 1.524940029
## [562,] 0.77444939 2.069445951
## [563,] 1.17349417 0.786787144
## [564,] 2.05361694 -0.061176133
## [565,] 2.34932352 -0.187690050
## [566,] 2.51423592 0.900613052
## [567,] 1.45187967 -0.559614382
## [568,] 5.34204239 -2.170617564
## [569,] 4.01616536 1.230238755
## [570,] 1.50579556 0.631334897
## [571,] 2.97268746 0.213392334
## [572,] 1.19192657 1.236468642
## [573,] 0.75895826 0.931771759
## [574,] 3.87741644 -0.437571703
## [575,] 2.70270048 0.870789366
## [576,] 2.82770952 -0.543354164
## [577,] 1.63292827 1.235686118
## [578,] 1.14888167 1.855000346
## [579,] 2.24822391 1.330031173
## [580,] 4.41017885 1.323660713
## [581,] 2.41104480 -0.473188274
## [582,] 2.55090017 1.437489797#Ugraph(P0.all.6$sparseParCor, type = "fancy", lay = "layout_in_circle",
# Vcolor = Colors, Vsize = 2, Vcex = .1, prune = T, cut = 0.5,
# main = "All Numerical data\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\nRed: miRNASeqGene; Blue: RNASeq2GeneNorm; Green: RPPA Array")GGM.all.6 = as.data.frame(GGMnetworkStats(P0.all.6$sparseParCor, as.table = T))
GGM.all.6.order = GGM.all.6[order(GGM.all.6$degree, decreasing = T), ]
#Output top 5%
GGM.all.6.order[1:round(nrow(GGM.all.6.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos
## TFRC.RPPA 246 14797.5257 0.0010857763 1.0000000 132 114
## FN1.R2Gn 236 12443.8642 0.0010718114 0.9457201 131 105
## FASN.RPPA 231 12054.2369 0.0010526316 0.9783767 114 117
## G6PD.RPPA 220 11507.5629 0.0010438413 0.9260943 114 106
## GAPDH.R2Gn 219 13510.0325 0.0010515247 0.8886630 121 98
## MYH11.RPPA 217 8518.6384 0.0010438413 0.9830667 108 109
## GATA3.RPPA 216 8182.4011 0.0010384216 0.9091354 118 98
## GAPDH.RPPA 214 11170.7529 0.0010362694 0.8851330 108 106
## PDCD4.RPPA 213 6679.3839 0.0010416667 0.9797947 104 109
## IGFBP2.RPPA 205 7040.7845 0.0010373444 0.9389363 108 97
## SQSTM1.R2Gn 189 5469.4256 0.0010111223 0.8873399 102 87
## HSPA1A.R2Gn 184 4968.5134 0.0009891197 0.7945652 83 101
## EEF2.R2Gn 181 3159.9552 0.0010121457 0.8839068 93 88
## ATM.RPPA 172 3993.4665 0.0010020040 0.8264090 84 88
## SYP.R2Gn 156 2202.8859 0.0009661836 0.8222125 80 76
## RPS6.R2Gn 153 3690.7506 0.0009737098 0.7168802 81 72
## TGM2.R2Gn 141 1958.3442 0.0009606148 0.7160260 72 69
## PTEN.RPPA 137 2597.9479 0.0009328358 0.6853480 78 59
## hsa-mir-122 135 1554.4340 0.0009157509 0.6267499 67 68
## CTNNB1.R2Gn 127 2018.0639 0.0009496676 0.6685270 58 69
## MSH6.RPPA 127 820.9801 0.0009578544 0.7322693 72 55
## RBM15.RPPA 125 1396.8686 0.0009442871 0.6570886 67 58
## SERPINE1.R2Gn 122 1089.1721 0.0009319664 0.6360909 59 63
## TTF1.RPPA 122 953.8346 0.0009216590 0.6117119 60 62
## hsa-mir-383 121 1517.4066 0.0009250694 0.6220908 71 50
## hsa-mir-137 120 1086.1332 0.0009389671 0.6863619 65 55
## hsa-mir-135a-2 118 655.2413 0.0009372071 0.6643085 58 60
## IGFBP2.R2Gn 115 1043.2269 0.0009259259 0.6057483 67 48
## hsa-mir-206 113 609.7036 0.0009107468 0.6208832 53 60
## hsa-mir-3923 112 963.3938 0.0009407338 0.6335249 47 65
## hsa-mir-577 112 729.4975 0.0009174312 0.6026398 54 58
## hsa-mir-216b 111 698.0320 0.0008865248 0.5858131 61 50
## COL6A1.R2Gn 110 1091.9639 0.0009354537 0.6032470 57 53
## EGFR.RPPA 105 674.7148 0.0009259259 0.5969085 57 48
## hsa-mir-216a 104 661.3425 0.0008960573 0.5560394 53 51
## mutualInfo variance partialVar
## TFRC.RPPA 0.25622527 1.292044 1
## FN1.R2Gn 0.17526080 1.191557 1
## FASN.RPPA 0.15241862 1.164648 1
## G6PD.RPPA 0.18646593 1.204984 1
## GAPDH.R2Gn 0.18534206 1.203630 1
## MYH11.RPPA 0.22591537 1.253470 1
## GATA3.RPPA 0.18283443 1.200616 1
## GAPDH.RPPA 0.24242772 1.274339 1
## PDCD4.RPPA 0.18953406 1.208686 1
## IGFBP2.RPPA 0.15876069 1.172057 1
## SQSTM1.R2Gn 0.12501668 1.133167 1
## HSPA1A.R2Gn 0.13556427 1.145183 1
## EEF2.R2Gn 0.13791771 1.147881 1
## ATM.RPPA 0.13585486 1.145516 1
## SYP.R2Gn 0.09556231 1.100277 1
## RPS6.R2Gn 0.08739767 1.091331 1
## TGM2.R2Gn 0.07119594 1.073792 1
## PTEN.RPPA 0.06466836 1.066805 1
## hsa-mir-122 0.05631111 1.057927 1
## CTNNB1.R2Gn 0.06430739 1.066420 1
## MSH6.RPPA 0.06546424 1.067655 1
## RBM15.RPPA 0.05650845 1.058136 1
## SERPINE1.R2Gn 0.05697306 1.058627 1
## TTF1.RPPA 0.05249557 1.053898 1
## hsa-mir-383 0.05710122 1.058763 1
## hsa-mir-137 0.05268007 1.054092 1
## hsa-mir-135a-2 0.05779898 1.059502 1
## IGFBP2.R2Gn 0.07063229 1.073187 1
## hsa-mir-206 0.05519122 1.056743 1
## hsa-mir-3923 0.04509524 1.046127 1
## hsa-mir-577 0.04391752 1.044896 1
## hsa-mir-216b 0.04931901 1.050555 1
## COL6A1.R2Gn 0.05208990 1.053470 1
## EGFR.RPPA 0.04995200 1.051221 1
## hsa-mir-216a 0.04801197 1.049183 1Colors.6.plot <- rownames(GGM.all.6.order)
Colors.6.plot[grep("hsa", rownames(GGM.all.6.order))] <- "miRNASeqGene"
Colors.6.plot[grep(".RPPA", rownames(GGM.all.6.order))] <- "RPPA Array"
Colors.6.plot[grep(".R2Gn", rownames(GGM.all.6.order))] <- "RNASeq2GeneNorm"ggplot(GGM.all.6.order, aes(x = reorder(rownames(GGM.all.6.order), -degree), y = degree, color = Colors.6.plot)) +
geom_point() +
geom_hline(yintercept = mean(GGM.all.6.order$degree), linetype = "dashed", color = "red") +
# 36th unit: top 5%
geom_hline(yintercept = GGM.all.6.order[36,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "All Data", guide = guide_axis(angle = 90)) +
scale_color_manual(values = c("red", "blue", "green3"))+
ggtitle("Variables sorted by degree, FDR = 1-1e-6")
2. Split data into alive and dead
A. RPPAArray
data.RPPA.alive = data.RPPA[which(data.Y == 0), ]
data.RPPA.dead = data.RPPA[which(data.Y == 1), ]set.seed(42)
opt.RPPA.alive = optPenalty.kCVauto(Y = data.RPPA.alive, lambdaMin = 1e-11, lambdaMax = 10)
opt.RPPA.dead = optPenalty.kCVauto(Y = data.RPPA.dead, lambdaMin = 1e-11, lambdaMax = 10)
opt.RPPA.alive$optLambda## [1] 0.002760197opt.RPPA.dead$optLambda## [1] 1.07822edgeHeat(opt.RPPA.alive$optPrec, diag = F, textsize = 7)## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
edgeHeat(opt.RPPA.dead$optPrec, diag = F, textsize = 7)## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
CNplot(covML(data.RPPA.alive),
lambdaMin = 1e-11,
lambdaMax = 1000,
step = 5000,
Iaids = T,
vertical = T,
value = opt.RPPA.alive$optLambda)## Perform input checks...
## Calculating spectral condition numbers...
## Calculating interpretational aids...
## Plotting...
CNplot(covML(data.RPPA.dead),
lambdaMin = 1e-11,
lambdaMax = 1000,
step = 5000,
Iaids = T,
vertical = T,
value = opt.RPPA.dead$optLambda)## Perform input checks...
## Calculating spectral condition numbers...
## Calculating interpretational aids...
## Plotting...
P0.RPPA.alive = sparsify(opt.RPPA.alive$optPrec, threshold = "localFDR", FDRcut=0.9)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 20
## - Corresponding to 3.79 % of possible edges
## P0.RPPA.dead = sparsify(opt.RPPA.dead$optPrec, threshold = "localFDR", FDRcut=0.9)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 53
## - Corresponding to 10.04 % of possible edges
## #dev.new(width = 20, height = 20, unit = "in", noRStudioGD = F)
set.seed(42)
Ugraph(P0.RPPA.alive$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 15, Vcex = 1, prune = T, cut = 0.5,
main = "RPPA Array data (Surviving patients)\nFDRcutoff at 0.9, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] 1.30312237 1.32763370
## [2,] -0.07806325 -3.27186354
## [3,] -1.24777005 0.39164499
## [4,] 1.95706628 -1.51097092
## [5,] 0.07130347 -0.38547109
## [6,] 3.75744057 -2.68475543
## [7,] 0.01030002 -2.26645778
## [8,] 4.24505379 -1.94809146
## [9,] 0.72295784 -2.97463671
## [10,] 0.88075495 -3.78946084
## [11,] 3.10337469 2.21813830
## [12,] 0.76365460 -1.35464647
## [13,] -0.03197742 0.84897079
## [14,] 3.66522379 0.67887394
## [15,] 0.42197737 1.80743202
## [16,] -0.50234629 1.80398368
## [17,] 4.42246045 0.03963785
## [18,] 2.65442045 1.33801337Ugraph(P0.RPPA.dead$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 15, Vcex = 1, prune = T, cut = 0.5,
main = "RPPA Array data (Deceased patients)\nFDRcutoff at 0.9, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] -3.90505735 1.2497719
## [2,] -1.07296319 3.5803678
## [3,] -5.42736857 1.2229008
## [4,] -4.03203136 0.1865667
## [5,] -4.65516344 3.0245029
## [6,] -2.65803881 3.1759668
## [7,] -4.26960810 4.4250582
## [8,] -4.36723719 1.1446965
## [9,] -1.80386461 4.1745790
## [10,] -4.04302517 2.7518117
## [11,] -2.62154828 -0.5392755
## [12,] -0.61903568 1.7461050
## [13,] -3.80160488 2.1268026
## [14,] -5.74559053 -0.3621092
## [15,] -6.04780972 4.0601347
## [16,] -1.83739003 -0.2445880
## [17,] 0.46528983 0.5759921
## [18,] 0.04871829 -0.4501331
## [19,] -2.45560743 2.2228319
## [20,] -3.15673665 2.9376634
## [21,] -3.03439655 1.2352369
## [22,] -0.80326510 -1.2197997
## [23,] -2.23298674 5.5522509
## [24,] -1.82733616 0.9257642
## [25,] -2.03439040 2.5813589#Ugraph(P0.RPPA.alive$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 15, Vcex = 1, prune = T, cut = 0.5,
# main = "RPPA Array data (Surviving patients)\nFDRcutoff at 0.9, Strong Edge cutoff at 0.5")
#Ugraph(P0.RPPA.dead$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 15, Vcex = 1, prune = T, cut = 0.5,
# main = "RPPA Array data (Deceased patients)\nFDRcutoff at 0.9, Strong Edge cutoff at 0.5")GGM.RPPA.alive = as.data.frame(GGMnetworkStats(P0.RPPA.alive$sparseParCor, as.table = T))## Warning in log(det(S[-j, -j] - S[-j, j, drop = FALSE] %*% S[j, -j, drop =
## FALSE]/S[j, : NaNs produced
## Warning in log(det(S[-j, -j] - S[-j, j, drop = FALSE] %*% S[j, -j, drop =
## FALSE]/S[j, : NaNs produced
## Warning in log(det(S[-j, -j] - S[-j, j, drop = FALSE] %*% S[j, -j, drop =
## FALSE]/S[j, : NaNs producedGGM.RPPA.dead = as.data.frame(GGMnetworkStats(P0.RPPA.dead$sparseParCor, as.table = T))
GGM.RPPA.alive.order = GGM.RPPA.alive[order(GGM.RPPA.alive$degree, decreasing = T), ]
GGM.RPPA.dead.order = GGM.RPPA.dead[order(GGM.RPPA.dead$degree, decreasing = T), ]
#Output top 10
GGM.RPPA.alive.order[1:10, ]## degree betweenness closeness eigenCentrality nNeg nPos mutualInfo
## GAPDH.RPPA 5 65.5 0.02631579 1.00000000 5 0 NaN
## G6PD.RPPA 3 44.0 0.02325581 0.73259940 3 0 -0.3139754
## IGFBP2.RPPA 3 6.5 0.01587302 0.14513745 3 0 NaN
## FASN.RPPA 3 36.0 0.01923077 0.17931623 3 0 -0.8602668
## PDCD4.RPPA 3 6.5 0.01587302 0.14513745 3 0 NaN
## AR.RPPA 3 54.0 0.02272727 0.23339006 3 0 1.0001768
## MYH11.RPPA 3 2.5 0.02173913 0.80492279 3 0 -2.2109076
## PREX1.RPPA 3 38.0 0.01960784 0.33451906 3 0 0.8678714
## ATM.RPPA 2 54.0 0.02500000 0.42234418 2 0 0.5043566
## TTF1.RPPA 2 0.0 0.01315789 0.09939752 2 0 -0.8732272
## variance partialVar
## GAPDH.RPPA -0.20806820 1
## G6PD.RPPA 0.73053696 1
## IGFBP2.RPPA -0.06925176 1
## FASN.RPPA 0.42304921 1
## PDCD4.RPPA -0.13146993 1
## AR.RPPA 2.71876244 1
## MYH11.RPPA 0.10960113 1
## PREX1.RPPA 2.38183549 1
## ATM.RPPA 1.65591980 1
## TTF1.RPPA 0.41760169 1GGM.RPPA.dead.order[1:10, ]## degree betweenness closeness eigenCentrality nNeg nPos mutualInfo
## TFRC.RPPA 12 101.160714 0.02702703 0.8167180 8 4 0.4711292
## FASN.RPPA 11 55.960065 0.02500000 1.0000000 6 5 0.4779378
## GATA3.RPPA 10 52.022727 0.02564103 0.9511864 7 3 0.4813064
## GAPDH.RPPA 9 37.683442 0.02564103 0.8982808 5 4 0.4945405
## G6PD.RPPA 8 35.913636 0.02439024 0.8158862 5 3 0.3275210
## MYH11.RPPA 8 22.307251 0.02325581 0.8439781 4 4 0.4890627
## PDCD4.RPPA 6 5.291775 0.02173913 0.7402321 6 0 0.1991657
## MSH6.RPPA 6 13.467857 0.02325581 0.6467117 4 2 0.2125263
## ATM.RPPA 5 5.325000 0.01923077 0.4910144 4 1 0.2830009
## RBM15.RPPA 4 8.733766 0.02040816 0.3764011 2 2 0.1717378
## variance partialVar
## TFRC.RPPA 1.601802 1
## FASN.RPPA 1.612745 1
## GATA3.RPPA 1.618187 1
## GAPDH.RPPA 1.639745 1
## G6PD.RPPA 1.387524 1
## MYH11.RPPA 1.630787 1
## PDCD4.RPPA 1.220384 1
## MSH6.RPPA 1.236799 1
## ATM.RPPA 1.327106 1
## RBM15.RPPA 1.187367 1ggplot(GGM.RPPA.alive.order, aes(x = reorder(rownames(GGM.RPPA.alive.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.RPPA.alive.order$degree), linetype = "dashed", color = "red") +
geom_hline(yintercept = 1, linetype = "dashed", color = "blue") +
scale_x_discrete(name = "RPPA Array", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-1, Surviving Patients")
ggplot(GGM.RPPA.dead.order, aes(x = reorder(rownames(GGM.RPPA.dead.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.RPPA.dead.order$degree), linetype = "dashed", color = "red") +
geom_hline(yintercept = 1, linetype = "dashed", color = "blue") +
scale_x_discrete(name = "RPPA Array", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-1, Deceased Patients")
B. RNASeq2GeneNorm
data.R2Gn.alive = data.R2Gn[which(data.Y == 0), ]
data.R2Gn.dead = data.R2Gn[which(data.Y == 1), ]set.seed(42)
opt.R2Gn.alive = optPenalty.kCVauto(Y = data.R2Gn.alive, lambdaMin = 1e-11, lambdaMax = 10)
opt.R2Gn.dead = optPenalty.kCVauto(Y = data.R2Gn.dead, lambdaMin = 1e-11, lambdaMax = 10)
opt.R2Gn.alive$optLambda## [1] 1.145441opt.R2Gn.dead$optLambda## [1] 1.809384edgeHeat(opt.R2Gn.alive$optPrec, diag = F, textsize = 1)## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
edgeHeat(opt.R2Gn.dead$optPrec, diag = F, textsize = 1)## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
CNplot(covML(data.R2Gn.alive),
lambdaMin = 1e-11,
lambdaMax = 1000,
step = 5000,
Iaids = T,
vertical = T,
value = opt.R2Gn.alive$optLambda)## Perform input checks...
## Calculating spectral condition numbers...
## Calculating interpretational aids...
## Plotting...
CNplot(covML(data.R2Gn.dead),
lambdaMin = 1e-11,
lambdaMax = 1000,
step = 5000,
Iaids = T,
vertical = T,
value = opt.R2Gn.dead$optLambda)## Perform input checks...
## Calculating spectral condition numbers...
## Calculating interpretational aids...
## Plotting...
Smallest possible FDRcut:
P0.R2Gn.alive.min = sparsify(opt.R2Gn.alive$optPrec, threshold = "localFDR", FDRcut=1-1e-14)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 539
## - Corresponding to 2.76 % of possible edges
## P0.R2Gn.dead.min = sparsify(opt.R2Gn.dead$optPrec, threshold = "localFDR", FDRcut=1-1e-14)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 739
## - Corresponding to 3.79 % of possible edges
## #dev.new(width = 20, height = 20, unit = "in", noRstudioGD = F)
set.seed(42)
Ugraph(P0.R2Gn.alive.min$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
main = "RNASeq2GeneNorm data (Surviving Patients)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] 4.8339418 -2.22741792
## [2,] 6.2388509 -4.59779911
## [3,] 3.9196054 -8.38430873
## [4,] 4.7239480 -1.01065361
## [5,] 6.7159234 0.29954974
## [6,] 3.0659461 -0.28888263
## [7,] 4.9284421 2.65519187
## [8,] 1.4987280 1.33931190
## [9,] 6.1382036 -0.58724540
## [10,] 8.7852378 3.36598489
## [11,] 4.2151258 -0.64568068
## [12,] 0.3169712 -7.73128784
## [13,] 3.2254296 5.42765454
## [14,] 4.5916356 -0.27168793
## [15,] 10.1223755 1.06048331
## [16,] 5.6576941 1.47016896
## [17,] 6.6409546 -0.71367502
## [18,] 0.9369151 -7.87017060
## [19,] 6.2908380 1.31326659
## [20,] 5.5969973 -6.52510410
## [21,] -0.7305915 -6.57858614
## [22,] 9.6136139 -2.10069074
## [23,] 5.8406175 0.70883725
## [24,] 3.9324587 -7.78230315
## [25,] 6.7972364 -0.21364725
## [26,] 1.5876710 -1.69945417
## [27,] -0.3051539 -7.18991693
## [28,] 5.6069907 -2.02488228
## [29,] 4.2482910 3.73111938
## [30,] -1.2328020 -2.73601675
## [31,] 5.7260344 3.82444694
## [32,] 1.3141803 -1.25717003
## [33,] 4.6147651 -4.79664840
## [34,] 4.0409597 -1.52089184
## [35,] 2.4150493 2.73958887
## [36,] -1.5684038 -3.33775683
## [37,] 6.2873628 -2.11270434
## [38,] 5.0677607 -3.35947872
## [39,] 7.3920941 1.45840527
## [40,] 3.2955089 -1.32977375
## [41,] 3.9588920 -6.83995502
## [42,] 0.1660350 -5.91654271
## [43,] 7.3676370 2.08681348
## [44,] 4.9456797 -0.29459057
## [45,] 5.6557729 0.25151289
## [46,] 4.9616204 0.52819853
## [47,] 2.7024621 -1.01232323
## [48,] 2.7219649 -7.79312257
## [49,] 4.3284913 5.54256061
## [50,] 0.7150397 -1.88271137
## [51,] 0.5894819 -1.38712389
## [52,] 1.4175007 -0.74011265
## [53,] 5.3926730 -1.27627708
## [54,] 1.1253622 -2.36777366
## [55,] 6.9413105 -3.11689562
## [56,] 3.4359849 0.10615328
## [57,] 2.0244515 -8.29583223
## [58,] 2.1319504 -0.01391224
## [59,] 1.0131440 -5.83262357
## [60,] 5.9255516 -0.10502874
## [61,] 6.4250837 -3.39432657
## [62,] 4.7387457 -1.28128592
## [63,] 5.6878739 -4.44966186
## [64,] 2.9760557 2.68458221
## [65,] 3.0521555 -0.84555003
## [66,] -1.1226310 -6.03017614
## [67,] 5.1600712 -7.77094023
## [68,] 6.3882810 3.59785917
## [69,] 8.1151628 3.96731470
## [70,] 1.4138978 -8.24855505
## [71,] 4.0907241 2.78024379
## [72,] 4.2746106 1.14256145
## [73,] 0.4350768 -7.11338597
## [74,] -0.5597416 -5.68626387
## [75,] 5.7405995 -1.50243015
## [76,] 2.7671841 -2.21616899
## [77,] 1.7990708 1.97104215
## [78,] 1.6800689 -0.04499183
## [79,] -1.4697413 -4.01933153
## [80,] 0.1801833 -5.18737408
## [81,] 0.9390134 -0.01471292
## [82,] 6.3723420 -1.03504070
## [83,] 10.6451745 -1.55756922
## [84,] 10.9973168 0.09075175
## [85,] 8.8924081 -0.74342994
## [86,] 4.0504458 -0.92613051
## [87,] 3.8775922 -2.30785448
## [88,] 4.5493312 -1.44594996
## [89,] 3.3441110 -7.42517938
## [90,] 3.9504658 -2.66566896
## [91,] 3.3247079 1.22564308
## [92,] 5.4807472 2.40535588
## [93,] 4.6327485 -8.17088329
## [94,] 2.9976228 -3.77471609
## [95,] 1.5908884 -2.77518699
## [96,] 7.5120538 0.49911184
## [97,] 0.6300948 -0.74280071
## [98,] 2.5629409 -0.37168623
## [99,] 3.7795470 -1.87170025
## [100,] 10.3009720 -0.10306836
## [101,] 6.8736093 -4.00108628
## [102,] 5.6128037 -7.40867507
## [103,] -0.2191843 -4.51475490
## [104,] 3.7393908 -0.69913014
## [105,] 5.4018807 -2.45824126
## [106,] 0.8749522 -6.56849783
## [107,] 4.4977777 -3.05073637
## [108,] 3.2372749 -8.33073178
## [109,] -0.0523965 -6.53579999
## [110,] 5.2817076 -0.25667216
## [111,] 5.9457858 -1.12730421
## [112,] 4.6509184 -0.76175206
## [113,] 4.8996358 -6.76135087
## [114,] 1.7725039 -6.48724811
## [115,] 6.1180344 -1.53675214
## [116,] 4.9836535 1.43752728
## [117,] 5.1351991 -0.69622751
## [118,] 2.6096487 -8.47729999
## [119,] -1.3942028 -5.37771766
## [120,] 9.3145923 2.66338357
## [121,] 3.8486416 -0.19509653
## [122,] 5.4306952 -1.05774613
## [123,] 5.6226442 -0.59034791
## [124,] -1.5727243 -4.64380212
## [125,] 1.2843363 -7.21794000
## [126,] 5.1056958 -1.68012481
## [127,] 4.1607160 0.29577525
## [128,] 2.7822775 -1.52869391
## [129,] 4.6659483 -1.82188424
## [130,] 4.5413667 -7.37939323
## [131,] 1.8844588 -7.58047868
## [132,] 2.8994662 3.53071241
## [133,] 6.2079425 2.32212878
## [134,] 6.1430041 -7.06257959
## [135,] 3.5346689 3.92048676
## [136,] 2.8672847 1.53445489
## [137,] 6.3659660 0.56825684
## [138,] 3.0152759 -6.61595299
## [139,] -0.8152298 -3.43947369
## [140,] -0.8534053 -4.99723758
## [141,] -0.8338246 -4.20852720
## [142,] 6.3887606 -6.39196624
## [143,] 4.1435411 -0.13345639
## [144,] 2.3743815 -7.04119878Ugraph(P0.R2Gn.dead.min$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
main = "RNASeq2GeneNorm data (Deceased Patients)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] 0.60136378 -1.39732942
## [2,] 0.59436511 -3.79246442
## [3,] -2.17981915 -4.05947409
## [4,] -1.18912163 -1.24802804
## [5,] 5.20827122 -4.87732619
## [6,] 2.29654829 -2.84069431
## [7,] -0.34525459 -8.39858645
## [8,] -0.34037504 -0.06225052
## [9,] -0.01786927 -7.59005509
## [10,] 1.77604114 -3.04941069
## [11,] 2.43357628 -4.98519626
## [12,] -0.11019578 -1.45012422
## [13,] 0.80383210 -4.76120462
## [14,] 3.35545268 -3.09141583
## [15,] -2.57168366 -1.78231123
## [16,] 1.57521764 0.92117748
## [17,] 3.79529595 -5.78095713
## [18,] -0.87200614 -5.10402273
## [19,] 1.88525553 -1.14285168
## [20,] 0.54059842 -2.66237538
## [21,] -2.94351106 -3.72163059
## [22,] 0.49744415 2.87376740
## [23,] -0.00109703 0.32482437
## [24,] -1.72075284 -4.88259414
## [25,] -2.20325730 3.72412556
## [26,] -1.94797259 -1.65073127
## [27,] 1.23237393 -3.17113382
## [28,] 1.27229173 -4.23034134
## [29,] 0.38238672 -2.89367915
## [30,] 1.93966120 -6.71278441
## [31,] -1.55653239 3.93449162
## [32,] 1.06085426 -0.78708964
## [33,] 7.94378256 -3.75572145
## [34,] 2.75726207 -1.09922561
## [35,] 3.25332143 -1.48995420
## [36,] 7.40109729 -5.41404314
## [37,] 1.49665403 -5.67910592
## [38,] 6.09550857 -1.21680766
## [39,] 3.01540178 2.29246862
## [40,] 2.20670557 -5.72408500
## [41,] 1.32671272 -2.40133164
## [42,] 0.82137485 -1.01228931
## [43,] -3.63954496 -6.27077966
## [44,] 0.62251535 -2.08955601
## [45,] -1.36512888 -3.00985635
## [46,] 3.91176841 -1.72507052
## [47,] 1.02906504 -2.14035075
## [48,] 0.79077806 -2.83351372
## [49,] 5.08185118 -1.50558197
## [50,] 1.21599210 -1.97092052
## [51,] 1.21067317 -1.31275770
## [52,] -3.16505805 2.96420653
## [53,] -0.93176604 -2.81332054
## [54,] 5.07874975 -3.63488840
## [55,] 4.34743297 -3.03717500
## [56,] -2.74693026 3.36483156
## [57,] 3.48456511 -4.17397757
## [58,] -2.48826432 -7.39626921
## [59,] 5.46903680 -2.51512447
## [60,] -0.14326601 -0.36621231
## [61,] -3.40146656 -1.44224045
## [62,] 2.73516310 -4.43813216
## [63,] 2.18552300 -1.14105094
## [64,] 0.48027250 1.95066826
## [65,] -0.58538378 -5.88853172
## [66,] 0.79435809 -1.76460979
## [67,] -0.43749083 -4.03663348
## [68,] 1.33052141 -0.95397311
## [69,] 0.41087379 -2.19081062
## [70,] 6.53746433 -6.48309715
## [71,] 0.00537019 -2.69685456
## [72,] 2.84099114 -2.65872165
## [73,] -4.04567881 -5.59770118
## [74,] 3.58398043 -7.25411733
## [75,] 0.68154509 0.08608182
## [76,] 3.96178924 -3.52357954
## [77,] 4.05764991 -2.55956199
## [78,] -3.95994626 2.07537647
## [79,] 5.68427255 -0.52901332
## [80,] 4.74583590 -6.45901964
## [81,] 1.37600481 -0.25847053
## [82,] 1.58481122 -1.43378046
## [83,] 7.95829789 -2.42932177
## [84,] 3.71679835 2.02799325
## [85,] 2.81289319 -3.25152411
## [86,] 3.55269933 -6.54476065
## [87,] -1.10332491 -1.70275768
## [88,] -0.83214436 -2.15945319
## [89,] -1.59862230 -5.88662841
## [90,] 7.01198700 -5.92007529
## [91,] -2.38179354 -5.15401300
## [92,] -4.26902846 1.44520698
## [93,] 2.28222078 0.49031051
## [94,] 2.37892782 -1.43791720
## [95,] 7.99624474 -3.13398194
## [96,] 3.04250974 -2.13933169
## [97,] 5.17376950 -3.05366147
## [98,] -1.13959284 -0.83299843
## [99,] 1.56932374 -2.61377073
## [100,] 7.88458865 -4.35693914
## [101,] -0.19489660 -2.03301463
## [102,] 1.80444767 -0.00688292
## [103,] 3.82110758 3.60030917
## [104,] 2.34291984 -3.75565967
## [105,] -1.09659619 -0.48032157
## [106,] 1.79128545 -0.71988710
## [107,] 0.15551843 -3.82991950
## [108,] 0.79153896 -6.22549127
## [109,] 4.64593324 0.34091124
## [110,] -0.13471479 -0.90306451
## [111,] 0.93864886 -3.76319812
## [112,] -1.98968829 -3.58933538
## [113,] 1.35531008 1.56399492
## [114,] 2.53322389 -1.72584053
## [115,] 7.55660848 -4.83294507
## [116,] -4.36889478 -4.81985908
## [117,] 2.52250504 -6.66218719
## [118,] 0.09118475 -1.86734524
## [119,] 4.33147204 -5.15350762
## [120,] -1.31264467 -7.18798419
## [121,] 0.34472370 -5.50033190
## [122,] 4.01259418 1.19062746
## [123,] 1.66867146 -3.91414521
## [124,] 2.42317404 -2.39958063
## [125,] -0.62476291 -1.18059775
## [126,] -3.12334936 -6.85238758
## [127,] 2.04106328 -2.25942868
## [128,] 2.84542622 -5.53801053
## [129,] 2.96415656 -0.21173630
## [130,] 2.96350814 -0.60487112
## [131,] 1.52716740 -0.75335927
## [132,] 2.21811143 -3.20852327
## [133,] 0.88263433 1.08961936
## [134,] 2.95505586 3.81048967
## [135,] 5.68104271 2.06148966
## [136,] -3.57490105 2.54974284
## [137,] 1.59685835 -1.78715858
## [138,] 1.76621723 -2.45008216
## [139,] 1.53355953 -3.49976242
## [140,] 2.03413499 -1.83750703
## [141,] 0.19436244 -3.45218829
## [142,] -1.61975410 -0.11179186
## [143,] -0.61727219 -2.59767792
## [144,] 5.44065796 -0.05177114
## [145,] 5.64835419 -4.14119551
## [146,] 1.67803916 -4.75448472
## [147,] -0.20154914 2.64049214
## [148,] -0.50659860 -2.04987702
## [149,] 2.35290835 -0.62916397
## [150,] 5.01005077 2.70515557
## [151,] -0.93737252 1.31451215
## [152,] -0.65296651 -3.16242448
## [153,] 4.24529382 -1.14633663
## [154,] 2.14934313 4.01100831
## [155,] -1.63250277 -2.51694161
## [156,] 3.13386081 -2.58439217
## [157,] 0.94880860 -2.51218261#Ugraph(P0.R2Gn.alive.min$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
# main = "RNASeq2GeneNorm data (Surviving Patiens)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5")
#Ugraph(P0.R2Gn.dead.min$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
# main = "RNASeq2GeneNorm data (Deceased Patiens)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5")GGM.R2Gn.alive.min = as.data.frame(GGMnetworkStats(P0.R2Gn.alive.min$sparseParCor, as.table = T))
GGM.R2Gn.dead.min = as.data.frame(GGMnetworkStats(P0.R2Gn.dead.min$sparseParCor, as.table = T))
GGM.R2Gn.alive.min.order = GGM.R2Gn.alive.min[order(GGM.R2Gn.alive.min$degree, decreasing = T), ]
GGM.R2Gn.dead.min.order = GGM.R2Gn.dead.min[order(GGM.R2Gn.dead.min$degree, decreasing = T), ]
#Output top 5%
GGM.R2Gn.alive.min.order[1:round(nrow(GGM.R2Gn.alive.min.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos mutualInfo
## GAPDH.R2Gn 104 6320.1007 0.005464481 1.0000000 47 57 0.21637603
## SQSTM1.R2Gn 61 1284.8660 0.004424779 0.8860031 35 26 0.09878604
## EEF2.R2Gn 41 871.3416 0.004032258 0.6698942 25 16 0.05263190
## TGM2.R2Gn 38 909.1790 0.004016064 0.6032739 17 21 0.04378580
## HSPA1A.R2Gn 38 487.9173 0.004032258 0.6811141 19 19 0.04737123
## IGFBP2.R2Gn 34 565.8591 0.003891051 0.6422618 21 13 0.04466094
## COL6A1.R2Gn 31 198.4089 0.003921569 0.6821071 15 16 0.03482934
## SYP.R2Gn 27 265.7875 0.003816794 0.5227951 15 12 0.02869397
## CTNNB1.R2Gn 25 108.3801 0.003731343 0.5874538 13 12 0.02540837
## CDH2.R2Gn 24 183.0774 0.003105590 0.4971453 16 8 0.01769934
## variance partialVar
## GAPDH.R2Gn 1.241569 1
## SQSTM1.R2Gn 1.103830 1
## EEF2.R2Gn 1.054042 1
## TGM2.R2Gn 1.044759 1
## HSPA1A.R2Gn 1.048511 1
## IGFBP2.R2Gn 1.045673 1
## COL6A1.R2Gn 1.035443 1
## SYP.R2Gn 1.029110 1
## CTNNB1.R2Gn 1.025734 1
## CDH2.R2Gn 1.017857 1GGM.R2Gn.dead.min.order[1:round(nrow(GGM.R2Gn.dead.min.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos mutualInfo
## FN1.R2Gn 81 2798.3855 0.004201681 1.0000000 49 32 0.4513300
## SYP.R2Gn 68 2016.8866 0.004081633 0.8879855 38 30 0.2169417
## EEF2.R2Gn 66 1399.8347 0.004016064 0.9268474 37 29 0.1837564
## GAPDH.R2Gn 62 1487.3920 0.003921569 0.8463917 33 29 0.2305904
## HSPA1A.R2Gn 61 1355.8966 0.003984064 0.8528902 37 24 0.2671855
## CTNNB1.R2Gn 58 1741.2118 0.003816794 0.7374888 29 29 0.1455934
## SQSTM1.R2Gn 57 960.8674 0.003906250 0.8354205 28 29 0.2015885
## RPS6.R2Gn 52 1082.3675 0.003802281 0.7763729 29 23 0.1746873
## CCND1.R2Gn 40 284.9175 0.003663004 0.7346627 19 21 0.1312334
## ADAR.R2Gn 37 273.7968 0.003496503 0.6672372 20 17 0.0903635
## variance partialVar
## FN1.R2Gn 1.570399 1
## SYP.R2Gn 1.242272 1
## EEF2.R2Gn 1.201723 1
## GAPDH.R2Gn 1.259343 1
## HSPA1A.R2Gn 1.306283 1
## CTNNB1.R2Gn 1.156726 1
## SQSTM1.R2Gn 1.223345 1
## RPS6.R2Gn 1.190874 1
## CCND1.R2Gn 1.140234 1
## ADAR.R2Gn 1.094572 1ggplot(GGM.R2Gn.alive.min.order, aes(x = reorder(rownames(GGM.R2Gn.alive.min.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.R2Gn.alive.min.order$degree), linetype = "dashed", color = "red") +
# 10th unit: top 5%
geom_hline(yintercept = GGM.R2Gn.alive.min.order[10,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-14, Surviving Patients")
ggplot(GGM.R2Gn.dead.min.order, aes(x = reorder(rownames(GGM.R2Gn.dead.min.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.R2Gn.dead.min.order$degree), linetype = "dashed", color = "red") +
# 10th unit: top 5%
geom_hline(yintercept = GGM.R2Gn.dead.min.order[10,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-14, Deceased Patients")
FDRcut 1-1e-6:
P0.R2Gn.alive.6 = sparsify(opt.R2Gn.alive$optPrec, threshold = "localFDR", FDRcut= 1-1e-6)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 883
## - Corresponding to 4.53 % of possible edges
## P0.R2Gn.dead.6 = sparsify(opt.R2Gn.dead$optPrec, threshold = "localFDR", FDRcut= 1-1e-6)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 1213
## - Corresponding to 6.22 % of possible edges
## #dev.new(width = 20, height = 20, unit = "in", noRstudioGD = F)
set.seed(42)
Ugraph(P0.R2Gn.alive.6$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
main = "RNASeq2GeneNorm data (Surviving Patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] 0.6626125 0.22468911
## [2,] -2.9474226 -2.44441443
## [3,] 1.4140883 -2.87541140
## [4,] -4.8168754 1.00793539
## [5,] -0.3444577 0.20897916
## [6,] -2.6715026 -0.05995879
## [7,] -1.1045375 -1.04594934
## [8,] -3.4172919 1.11422809
## [9,] -4.2517857 3.01169176
## [10,] 0.4390078 0.09729709
## [11,] -4.5552175 -0.91324303
## [12,] -1.1616016 0.22413829
## [13,] 2.0052218 -1.65962093
## [14,] -4.9010147 -2.32009518
## [15,] -2.5078665 -0.91657816
## [16,] 3.3874139 4.05328681
## [17,] -2.9084504 -0.68100239
## [18,] -1.7135563 -0.20514625
## [19,] 5.1966533 -3.54389476
## [20,] 0.1809289 -0.73117953
## [21,] 0.2133673 -2.12637691
## [22,] 4.9613471 -4.39907119
## [23,] -3.0074818 3.37628123
## [24,] -6.7230637 -3.80625426
## [25,] -2.3675244 1.06511317
## [26,] -1.4226696 -3.93925624
## [27,] -1.4497001 -0.90844565
## [28,] 1.1085179 -1.44820872
## [29,] 1.1289153 -6.55621704
## [30,] -1.0743340 1.34388257
## [31,] -4.6130038 0.40219481
## [32,] 0.7779442 -4.55233484
## [33,] -0.0832558 -3.72920913
## [34,] -4.6459028 0.01947182
## [35,] 1.7312776 -0.26939100
## [36,] 3.0475157 0.55761072
## [37,] 0.1122174 -2.68148227
## [38,] -1.2791569 1.00420260
## [39,] -3.2982086 2.12327265
## [40,] 1.7816080 -1.04241869
## [41,] -0.3846561 -0.33008214
## [42,] -2.3897499 -1.82406918
## [43,] -2.7422829 1.92866744
## [44,] -0.7908125 -1.71373708
## [45,] 3.2368339 -5.27277029
## [46,] -3.2674246 -1.62733235
## [47,] 1.3355722 3.64663855
## [48,] -2.0628172 0.01543025
## [49,] -2.3458896 0.28874152
## [50,] -1.8833103 0.61539564
## [51,] -0.6708106 1.14361842
## [52,] -2.4084407 -5.03064827
## [53,] -3.4069402 -3.33535254
## [54,] -2.1694505 5.51845721
## [55,] -1.6185683 -5.26283331
## [56,] -2.5547981 -2.24674843
## [57,] -7.1392644 -2.89706777
## [58,] 1.0296150 -0.14830118
## [59,] 0.1034231 -6.78190768
## [60,] -0.2492421 1.39002543
## [61,] -1.9598868 -0.64665638
## [62,] 0.7105744 -0.72059639
## [63,] 0.8725143 1.60729706
## [64,] -1.8036370 0.91000580
## [65,] 2.0231064 -2.24471793
## [66,] 0.5391788 1.89373383
## [67,] 5.6441216 -1.26746925
## [68,] -1.8060333 -1.09022859
## [69,] -1.0387251 1.72568452
## [70,] -0.2266317 -0.01100187
## [71,] -2.2316883 -2.74211454
## [72,] -0.2080312 -1.37140807
## [73,] 2.9844693 -6.53755395
## [74,] -0.1739923 -0.56516390
## [75,] 3.5002154 -6.12994762
## [76,] 4.9394258 -5.08629899
## [77,] 2.3734597 -6.73439061
## [78,] -6.0922922 -1.04246092
## [79,] -1.9609603 -4.97608346
## [80,] -1.0867523 4.21219255
## [81,] 3.8304934 -1.29191614
## [82,] -6.8030959 2.37538406
## [83,] 1.6558204 1.67302222
## [84,] -3.9050025 1.00251785
## [85,] -2.5332779 -1.41459149
## [86,] -4.8094717 -4.49078935
## [87,] 2.3316812 -0.81134321
## [88,] 3.8660393 -5.48451755
## [89,] -1.8471057 -1.85417945
## [90,] -1.0761207 -1.62955286
## [91,] -1.3717205 2.44812531
## [92,] -0.6173523 -2.52973554
## [93,] 4.2323817 -5.88744363
## [94,] -0.3533584 -4.58413212
## [95,] 2.8795135 1.68105600
## [96,] -2.0103998 -4.06777873
## [97,] -1.7974099 0.32730188
## [98,] 5.6958785 -3.16628270
## [99,] -2.0838620 2.58234784
## [100,] -4.2974548 -1.51990380
## [101,] -0.5729691 2.60962777
## [102,] -0.8142015 -0.53367893
## [103,] -3.0192971 -0.29495962
## [104,] -1.6960477 -0.45439524
## [105,] 0.7070984 -6.91669210
## [106,] 0.7660227 0.66562673
## [107,] -1.2435573 5.64858369
## [108,] -3.0381825 0.37059329
## [109,] -2.2880366 -0.59179953
## [110,] 2.2812470 -6.02231227
## [111,] 0.7811006 -1.98174939
## [112,] -1.8237541 -3.01699525
## [113,] -1.6746779 -2.40962758
## [114,] 0.6108266 -3.07248131
## [115,] -1.0761995 -2.59353440
## [116,] 3.7915041 -4.69687653
## [117,] -0.0723502 0.95712931
## [118,] 1.4886033 0.24344837
## [119,] -6.0013715 -0.44082896
## [120,] 0.6798923 3.90446780
## [121,] -0.7848846 -3.88465612
## [122,] 3.6919156 -1.81169087
## [123,] 5.5134963 -1.87855555
## [124,] 4.3839081 -5.02680621
## [125,] -0.6467793 -0.85944098
## [126,] 0.3177152 0.94777153
## [127,] 3.9361621 -0.09336302
## [128,] -0.5472480 1.64780797
## [129,] 2.6891176 1.36981736
## [130,] 1.6286578 0.86475858
## [131,] -1.0277246 -3.10151781
## [132,] -1.4304181 -0.19620758
## [133,] -1.1057923 -1.34163268
## [134,] -1.1238583 0.47056531
## [135,] 0.1136398 2.02723621
## [136,] 3.8531213 -0.79953343
## [137,] -1.8443485 1.30266529
## [138,] -2.0849315 -1.33687773
## [139,] -0.9549265 -0.78643667
## [140,] -1.1556349 -5.09782782
## [141,] 5.5010625 -4.03027059
## [142,] -4.9509551 3.01931603
## [143,] 0.2962771 5.34700416
## [144,] 4.6274547 -3.51865941
## [145,] -0.8026434 0.01369267
## [146,] -1.4413156 -0.62988288
## [147,] -2.2362135 -0.89780158
## [148,] -2.8132300 -4.70951189
## [149,] 4.3499917 -4.19968078
## [150,] -0.3962128 -0.90097141
## [151,] -0.7334097 0.43607723
## [152,] -1.5021528 0.08608431
## [153,] -0.4459254 0.90075408
## [154,] 1.7153064 -6.32025526
## [155,] -3.0511929 5.25622245
## [156,] 5.1996984 -2.69607381
## [157,] -4.6137941 1.35077931
## [158,] -3.8718296 1.92239498
## [159,] 2.8294064 -5.79993800
## [160,] -2.5277754 0.50498178
## [161,] -2.1267187 2.00023099
## [162,] 2.3331812 2.02644846
## [163,] 4.1674580 3.34689725
## [164,] -1.4399198 -1.26237032
## [165,] -2.6101930 -3.73401098
## [166,] 4.2173051 -0.48084627
## [167,] -4.1282302 -0.22828612
## [168,] 3.9934899 0.42804043
## [169,] 2.7972917 0.96155663
## [170,] 1.6741885 -6.96817023
## [171,] 2.2852084 0.05362223
## [172,] -1.0928814 0.69821003
## [173,] 5.7992164 -2.40254935Ugraph(P0.R2Gn.dead.6$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
main = "RNASeq2GeneNorm data (Deceased Patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] -1.08124046 -2.06484802
## [2,] -1.66915249 -3.73180174
## [3,] -0.29119982 0.69835515
## [4,] -2.17088815 -2.76791541
## [5,] -1.67520177 -5.86662252
## [6,] -1.47303743 -2.08236067
## [7,] 3.57431264 -0.24941017
## [8,] 1.03585723 -2.89733786
## [9,] 4.37268212 -5.55427976
## [10,] 0.67179072 -3.15876915
## [11,] -2.93225520 -2.02486845
## [12,] 0.43071694 -2.70856766
## [13,] -0.53560550 -0.54631388
## [14,] -2.54995686 -2.84195545
## [15,] -1.90458360 0.25534346
## [16,] -0.74671986 -5.23251328
## [17,] -2.10026850 -3.69441102
## [18,] -2.13716156 -2.09810913
## [19,] 1.30221313 -0.29957954
## [20,] -0.26561320 -4.07817533
## [21,] 0.02438068 -2.20104271
## [22,] -4.18224699 -0.37668433
## [23,] 4.82947483 -5.09912520
## [24,] 0.88504600 0.41640588
## [25,] 2.13513573 -3.86333295
## [26,] -0.82180465 -4.53876190
## [27,] 2.63888230 0.67212675
## [28,] 2.42806923 -2.65657213
## [29,] -7.38872944 -4.54309047
## [30,] -2.63533972 -3.94695730
## [31,] -2.60807735 -8.71927985
## [32,] -0.14461879 -2.60723330
## [33,] 1.29907771 -2.61301018
## [34,] -0.56357907 -1.29677120
## [35,] -4.60599718 -3.67958893
## [36,] -3.91795405 -0.68939156
## [37,] -0.67531369 -3.26755524
## [38,] -6.98869505 -4.88285926
## [39,] -4.90196891 -3.32148790
## [40,] -0.48986833 -3.94596935
## [41,] -1.71649649 -0.19073019
## [42,] 5.17667055 -3.25053448
## [43,] -2.61743398 -5.54990365
## [44,] -2.58000130 0.36168094
## [45,] -0.95326473 -5.78604447
## [46,] -4.10105642 -1.57263927
## [47,] -2.03437695 -4.12003537
## [48,] -1.15151047 -2.89995616
## [49,] -7.58862056 -2.72433672
## [50,] -0.39845138 -3.58821347
## [51,] -4.31885057 -2.16796444
## [52,] -0.75217684 -1.35880032
## [53,] -0.95416327 3.46914681
## [54,] 0.86264301 -0.58180117
## [55,] -2.32590497 -0.32213660
## [56,] -0.68324924 -2.59308800
## [57,] -0.10187853 -2.84518512
## [58,] -3.48030000 -3.13870822
## [59,] -0.88812663 -2.82534038
## [60,] -0.15356777 -3.80762597
## [61,] -1.19371232 2.28823141
## [62,] 0.32748783 -8.29086400
## [63,] 0.30460150 -1.39708869
## [64,] -3.15081643 -5.86427990
## [65,] -2.59298556 -2.32386171
## [66,] -1.99821647 -6.43405049
## [67,] -0.86868070 0.35430529
## [68,] -3.10607030 3.02936631
## [69,] -2.46362657 0.01327254
## [70,] -2.07591762 0.82677645
## [71,] -0.62337812 -3.04296689
## [72,] 0.87259432 0.98296732
## [73,] 0.79549603 -2.39143097
## [74,] -1.29089482 -3.72657698
## [75,] -0.19222987 1.07143877
## [76,] 2.24315514 -0.85671689
## [77,] -0.57433942 -2.30314605
## [78,] 0.42818813 -0.70101456
## [79,] -0.97315149 -3.98441435
## [80,] -0.60720995 -1.54776607
## [81,] -5.33677744 -0.57716298
## [82,] 0.13433981 -1.59280588
## [83,] 2.68464646 0.39889108
## [84,] -1.36845841 -2.72384008
## [85,] -4.40064509 2.45703330
## [86,] 3.34443529 -4.15562494
## [87,] 1.40248234 -3.18662119
## [88,] -1.16546490 -4.38779920
## [89,] 3.52685580 -7.01453072
## [90,] -1.98381042 -1.38742204
## [91,] 1.68280445 -1.31558098
## [92,] -3.01419467 -0.46645104
## [93,] -3.30522575 -3.84776066
## [94,] 0.15272410 -4.04054652
## [95,] 0.26544334 -3.32269748
## [96,] -7.35570394 -1.36701966
## [97,] -7.40307513 -3.28718205
## [98,] -0.72335519 -6.33700134
## [99,] -2.96326472 -3.17161549
## [100,] -3.08134470 -1.26507827
## [101,] -1.73803598 -4.45697986
## [102,] 0.66679080 -1.34136292
## [103,] 1.76944571 -0.41453382
## [104,] -6.74192527 -5.68607412
## [105,] 0.76983021 0.03368888
## [106,] -3.86023840 0.51501041
## [107,] 1.13064701 -4.58725690
## [108,] -1.19911000 -3.36074482
## [109,] -4.85377225 -2.65638850
## [110,] -2.13952506 -0.77594985
## [111,] -4.20446489 -4.90097917
## [112,] -0.11365707 -0.39463804
## [113,] 1.69875901 -7.78408759
## [114,] -0.95960672 -2.39980624
## [115,] -3.09832760 -1.68851408
## [116,] -1.04137261 -1.10670896
## [117,] -7.52744804 -3.89666896
## [118,] 0.14628663 -4.90893134
## [119,] -0.97396052 1.37837624
## [120,] -1.51952642 -1.23164048
## [121,] 1.95750827 -3.48036946
## [122,] -7.58200194 -2.07428749
## [123,] -1.32128911 -1.83795718
## [124,] 0.07714465 -0.84136911
## [125,] 1.25511530 -5.23596042
## [126,] 1.97997552 -5.19766630
## [127,] -0.44274444 -0.91258938
## [128,] -0.09057958 -1.90811004
## [129,] 2.76570051 -1.96358931
## [130,] -0.48121193 -5.56084107
## [131,] -2.31294157 -3.26227176
## [132,] -3.59275434 -6.12837396
## [133,] -1.55608871 2.08399535
## [134,] -3.12120982 -4.53756031
## [135,] 0.56297147 -3.42093622
## [136,] -1.15471187 0.19085991
## [137,] 3.86677259 -3.89745986
## [138,] 1.77999440 -4.56020781
## [139,] 1.53939661 -4.06398618
## [140,] -2.30489124 3.36234026
## [141,] -0.83786770 -0.36235807
## [142,] -1.60541605 -3.37775370
## [143,] 1.60156371 -1.81389853
## [144,] -3.69620999 2.99295299
## [145,] -0.33926984 -2.82853997
## [146,] -1.28794997 -0.22730747
## [147,] -0.13735924 -5.83293311
## [148,] 0.51526664 -3.79121994
## [149,] 0.01604318 -3.53551724
## [150,] -0.92980082 -3.71802486
## [151,] 2.66751632 -1.15067499
## [152,] -1.64501731 -5.19065895
## [153,] 0.48079849 0.53668874
## [154,] -4.69288891 -5.96844986
## [155,] 2.20697787 -6.06025787
## [156,] 0.20131191 -3.01573539
## [157,] -0.14392554 -3.31335582
## [158,] 0.16485717 -1.91293611
## [159,] 3.79224399 -1.99463365
## [160,] -2.38026660 -1.64987387
## [161,] -1.12824041 -1.59631572
## [162,] -2.70241969 -4.83125693
## [163,] -1.81617547 -2.86777711
## [164,] -3.66967577 -1.94627568
## [165,] -3.25042051 -0.99310058
## [166,] -2.65378171 -6.27045207
## [167,] 1.04169326 -1.69094286
## [168,] -3.31500061 0.22599407
## [169,] 0.84710285 -2.01475098
## [170,] -4.54457537 -4.29304495
## [171,] -0.47384444 -4.55737992
## [172,] 0.06929054 -7.34786166
## [173,] 0.74504961 -5.87891021
## [174,] 1.44390101 -2.16642216
## [175,] -3.56308691 -4.11099825
## [176,] -3.88737872 -2.96691225
## [177,] 3.29473862 -2.67104168
## [178,] 1.04686514 -1.05509293
## [179,] -1.77014304 -2.34128513
## [180,] -0.30909952 -1.86700354#Ugraph(P0.R2Gn.alive.6$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
# main = "RNASeq2GeneNorm data (Surviving Patiens)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")
#Ugraph(P0.R2Gn.dead.6$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
# main = "RNASeq2GeneNorm data (Deceased Patiens)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")GGM.R2Gn.alive.6 = as.data.frame(GGMnetworkStats(P0.R2Gn.alive.6$sparseParCor, as.table = T))
GGM.R2Gn.dead.6 = as.data.frame(GGMnetworkStats(P0.R2Gn.dead.6$sparseParCor, as.table = T))
GGM.R2Gn.alive.6.order = GGM.R2Gn.alive.6[order(GGM.R2Gn.alive.6$degree, decreasing = T), ]
GGM.R2Gn.dead.6.order = GGM.R2Gn.dead.6[order(GGM.R2Gn.dead.6$degree, decreasing = T), ]
#Output top 5%
GGM.R2Gn.alive.6.order[1:round(nrow(GGM.R2Gn.alive.6.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos mutualInfo
## GAPDH.R2Gn 129 7198.8489 0.004651163 1.0000000 58 71 0.21508889
## SQSTM1.R2Gn 81 2049.3061 0.003787879 0.8321434 43 38 0.10067626
## IGFBP2.R2Gn 62 1283.3031 0.003521127 0.6934990 35 27 0.05000158
## TGM2.R2Gn 62 1482.6744 0.003546099 0.6306330 34 28 0.04764892
## HSPA1A.R2Gn 53 512.0792 0.003401361 0.6909312 24 29 0.04861295
## EEF2.R2Gn 50 723.6905 0.003378378 0.6365496 27 23 0.05356719
## COL6A1.R2Gn 48 290.2947 0.003367003 0.6954044 24 24 0.03679409
## ERBB3.R2Gn 44 573.6492 0.003300330 0.4926954 17 27 0.02392273
## SYP.R2Gn 43 491.2374 0.003289474 0.5758292 23 20 0.03093353
## CDH2.R2Gn 40 196.6319 0.003205128 0.5904205 21 19 0.01957613
## variance partialVar
## GAPDH.R2Gn 1.239972 1
## SQSTM1.R2Gn 1.105919 1
## IGFBP2.R2Gn 1.051273 1
## TGM2.R2Gn 1.048802 1
## HSPA1A.R2Gn 1.049814 1
## EEF2.R2Gn 1.055028 1
## COL6A1.R2Gn 1.037479 1
## ERBB3.R2Gn 1.024211 1
## SYP.R2Gn 1.031417 1
## CDH2.R2Gn 1.019769 1GGM.R2Gn.dead.6.order[1:round(nrow(GGM.R2Gn.dead.6.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos mutualInfo
## FN1.R2Gn 103 3102.4423 0.003891051 1.0000000 61 42 0.4503771
## GAPDH.R2Gn 94 2170.2085 0.003745318 0.9484897 49 45 0.2350435
## SYP.R2Gn 91 1903.2029 0.003745318 0.9517211 51 40 0.2169166
## SQSTM1.R2Gn 90 1505.2766 0.003731343 0.9930780 45 45 0.1992638
## HSPA1A.R2Gn 86 1774.1413 0.003676471 0.9177083 53 33 0.2687390
## EEF2.R2Gn 85 1506.3636 0.003623188 0.9587648 47 38 0.1831855
## CTNNB1.R2Gn 76 1340.7157 0.003448276 0.8074700 43 33 0.1447713
## RPS6.R2Gn 75 1165.9741 0.003496503 0.8430660 42 33 0.1783714
## ADAR.R2Gn 58 366.7931 0.003289474 0.7450061 32 26 0.0918566
## CCND1.R2Gn 52 308.7403 0.003267974 0.7207221 23 29 0.1315518
## variance partialVar
## FN1.R2Gn 1.568904 1
## GAPDH.R2Gn 1.264964 1
## SYP.R2Gn 1.242240 1
## SQSTM1.R2Gn 1.220504 1
## HSPA1A.R2Gn 1.308314 1
## EEF2.R2Gn 1.201037 1
## CTNNB1.R2Gn 1.155775 1
## RPS6.R2Gn 1.195269 1
## ADAR.R2Gn 1.096208 1
## CCND1.R2Gn 1.140597 1ggplot(GGM.R2Gn.alive.6.order, aes(x = reorder(rownames(GGM.R2Gn.alive.6.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.R2Gn.alive.6.order$degree), linetype = "dashed", color = "red") +
# 10th unit: top 5%
geom_hline(yintercept = GGM.R2Gn.alive.6.order[10,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-6, Surviving Patients")
ggplot(GGM.R2Gn.dead.6.order, aes(x = reorder(rownames(GGM.R2Gn.dead.6.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.R2Gn.dead.6.order$degree), linetype = "dashed", color = "red") +
# 10th unit: top 5%
geom_hline(yintercept = GGM.R2Gn.dead.6.order[10,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-6, Deceased Patients")
C. miRNASeqGene
data.mRSG.alive = data.mRSG[which(data.Y == 0), ]
data.mRSG.dead = data.mRSG[which(data.Y == 1), ]set.seed(42)
opt.mRSG.alive = optPenalty.kCVauto(Y = data.mRSG.alive, lambdaMin = 1e-11, lambdaMax = 10)
opt.mRSG.alive$optLambda## [1] 1.662094set.seed(42)
# Warning: If lambdaMax is set to 1000, lambda will be stuck at 1000
# Setting lambdaMax = 10 avoids this issue
opt.mRSG.dead = optPenalty.kCVauto(Y = data.mRSG.dead, lambdaMin = 1e-11, lambdaMax = 10)
#opt.mRSG.dead.10 = optPenalty.kCVauto(Y = data.mRSG.dead, lambdaMin = 1e-11, lambdaMax = 1000, fold = 10)
#opt.mRSG.dead.5 = optPenalty.kCVauto(Y = data.mRSG.dead, lambdaMin = 1e-11, lambdaMax = 1000, fold = 5)
#opt.mRSG.dead.3 = optPenalty.kCVauto(Y = data.mRSG.dead, lambdaMin = 1e-11, lambdaMax = 1000, fold = 3)
#setNames(c(3,5,10,43), c(opt.mRSG.dead.3$optLambda, opt.mRSG.dead.5$optLambda, opt.mRSG.dead.10$optLambda, opt.mRSG.dead$optLambda))
opt.mRSG.dead$optLambda## [1] 1.079529edgeHeat(opt.mRSG.alive$optPrec, diag = F, textsize = 1)## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
edgeHeat(opt.mRSG.dead$optPrec, diag = F, textsize = 1)## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
Smallest possible FDRcut:
P0.mRSG.alive.min = sparsify(opt.mRSG.alive$optPrec, threshold = "localFDR", FDRcut=1-1e-13)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 1517
## - Corresponding to 1.38 % of possible edges
## P0.mRSG.dead.min = sparsify(opt.mRSG.dead$optPrec, threshold = "localFDR", FDRcut=1-1e-13)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 1684
## - Corresponding to 1.53 % of possible edges
## set.seed(42)
Ugraph(P0.mRSG.alive.min$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 2, Vcex = .3, prune = T, cut = 0.5, main = "miRNASeqGene data (Surviving Patients)\nFDRcutoff at 1-1e-13, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] 4.935464995 -0.95010258
## [2,] -6.356090756 7.87346558
## [3,] -2.575691947 1.38449284
## [4,] -3.794261835 -0.43068326
## [5,] -0.446362060 3.30032005
## [6,] -5.460130722 -3.59724432
## [7,] -4.966215468 -3.29126334
## [8,] -1.579816239 -2.55498813
## [9,] -3.001379615 -2.53600590
## [10,] -1.996251039 2.55458836
## [11,] 1.899743005 -0.86603179
## [12,] 0.585340317 0.63579584
## [13,] -2.336666951 0.82141166
## [14,] -0.596473373 1.27240717
## [15,] -0.030091979 2.91167239
## [16,] -1.669723998 0.77049357
## [17,] -1.898613089 3.33064813
## [18,] 1.526658613 -1.86507839
## [19,] -7.070045706 -0.61683749
## [20,] -1.772002372 1.83993354
## [21,] -8.341486045 -2.39103124
## [22,] -3.108964900 1.82752410
## [23,] 1.762183582 3.59002519
## [24,] -2.258129786 4.41582689
## [25,] -3.044241288 4.03801119
## [26,] -1.789130043 2.69232949
## [27,] -3.596002290 0.80065708
## [28,] -3.360005205 5.29258786
## [29,] -4.155062202 2.07095976
## [30,] 1.536848928 1.56354971
## [31,] -0.863211998 1.70901113
## [32,] -3.085639725 3.28991342
## [33,] -0.788624643 4.36180405
## [34,] -3.843876240 1.60917792
## [35,] -1.201562336 1.91085409
## [36,] -1.168230191 0.24278027
## [37,] -0.941050994 -0.94353871
## [38,] -0.532674575 1.95165122
## [39,] 0.779851022 1.78829495
## [40,] -2.928890111 2.23445458
## [41,] -0.952512418 3.24869286
## [42,] 0.726060581 3.34790648
## [43,] -7.643683295 3.09336019
## [44,] 1.002003046 7.44349735
## [45,] -2.384535324 1.21264626
## [46,] -2.869918950 0.36830509
## [47,] -2.611891601 7.42926492
## [48,] -3.059042421 7.26360322
## [49,] -1.441354475 3.18579376
## [50,] -2.023534796 1.92660501
## [51,] -1.944332430 -0.13743514
## [52,] -0.981700942 5.07776480
## [53,] 1.963346899 8.31291809
## [54,] 2.885169342 7.92515718
## [55,] 2.908087574 2.35100292
## [56,] 0.566552755 3.10971021
## [57,] 0.397794094 4.12591268
## [58,] -0.182018529 3.23734941
## [59,] -0.481870422 5.18144351
## [60,] -2.073937610 3.83075705
## [61,] -1.421190326 0.96011827
## [62,] -8.139571683 3.61965921
## [63,] 1.251554222 3.54669420
## [64,] -3.015830554 2.85377886
## [65,] -1.975396880 1.14465935
## [66,] -5.238364556 2.62270323
## [67,] -0.532345946 4.09036254
## [68,] 0.674465721 9.18864361
## [69,] 3.013213966 -0.82118883
## [70,] -5.794006759 -2.08226146
## [71,] -0.193849328 1.59524003
## [72,] 1.072784236 0.61520895
## [73,] -8.064993391 -3.09205262
## [74,] 0.001184256 2.43611256
## [75,] 0.849030401 5.20352883
## [76,] -3.227204289 1.38582167
## [77,] 0.550290553 3.66168012
## [78,] -1.030062213 6.69496899
## [79,] -1.406155568 4.39249993
## [80,] 0.548423212 2.62275918
## [81,] -0.941660603 3.99027509
## [82,] -2.898849730 4.95676378
## [83,] -1.551536916 1.57456127
## [84,] -4.277516964 3.84821493
## [85,] -2.567454903 2.46040420
## [86,] -3.254276645 0.32371920
## [87,] -2.866291564 3.95765163
## [88,] -2.125507814 -7.89354225
## [89,] -2.910356911 -6.29030715
## [90,] -2.619577437 -3.25123242
## [91,] -4.049373606 -1.88764050
## [92,] -3.723427523 2.34981742
## [93,] 0.477806544 2.23901853
## [94,] 2.051133385 1.29897957
## [95,] -0.727863018 3.83153455
## [96,] -3.328835686 1.99504946
## [97,] -2.495301136 1.72912011
## [98,] 0.095175454 3.72796344
## [99,] -8.396629089 0.80457223
## [100,] 3.738724514 0.79665331
## [101,] -3.712219749 2.63094175
## [102,] -3.224783823 2.50654745
## [103,] -2.497147912 3.22607252
## [104,] -1.033849219 -0.08842666
## [105,] -1.031783656 1.06827644
## [106,] -0.836162934 1.11236086
## [107,] -2.208370565 1.68265835
## [108,] -1.088435343 2.92184863
## [109,] -0.719433814 3.51114831
## [110,] -4.894302230 -4.57369500
## [111,] -4.526039261 -2.03463402
## [112,] -7.691118112 -5.65077575
## [113,] -3.020503572 -2.16308835
## [114,] -0.123332298 3.59815551
## [115,] -0.198986464 9.37932643
## [116,] -3.312464652 3.67192724
## [117,] 2.668809385 0.50874955
## [118,] -1.055206594 3.52129679
## [119,] -0.198740316 4.89444621
## [120,] 3.580104511 4.14510313
## [121,] -2.910344124 1.05236616
## [122,] -1.596486648 2.57033423
## [123,] -4.691930602 -3.61203767
## [124,] -0.490634941 2.37658133
## [125,] -2.367326469 5.00395716
## [126,] 3.028522092 -4.10372185
## [127,] 2.676558000 -1.33955262
## [128,] -0.256457486 1.09597398
## [129,] 1.541568613 2.61609528
## [130,] -4.730780656 -6.28901773
## [131,] -3.812748747 -5.67830739
## [132,] -3.768087136 -7.85830168
## [133,] 0.052304645 0.04165587
## [134,] -1.653781133 3.89119055
## [135,] -2.173812855 2.76143076
## [136,] -3.129407449 -3.96611542
## [137,] -2.636153649 2.84615866
## [138,] -2.727319668 1.80705964
## [139,] 5.659339881 5.74188603
## [140,] 4.720429245 6.19696333
## [141,] 6.781406218 6.34324558
## [142,] 6.291684502 7.25287261
## [143,] 6.673876190 7.17967627
## [144,] 6.862332189 6.79553344
## [145,] 4.185431867 5.54072897
## [146,] -6.838843962 2.51463304
## [147,] 4.879286292 5.90158096
## [148,] 4.560576909 5.64895709
## [149,] 5.853711981 4.78727630
## [150,] 6.192867409 7.61545141
## [151,] 5.611057028 6.26858418
## [152,] 6.028433065 5.65896210
## [153,] 5.806156525 5.30833550
## [154,] 5.264036479 5.81070840
## [155,] -0.414935170 0.68634929
## [156,] -0.658332848 0.18244971
## [157,] 0.261602098 -0.32903156
## [158,] -0.362892611 2.52259504
## [159,] -0.506497001 0.90748549
## [160,] -1.675555474 3.01724419
## [161,] -1.569810084 -0.70227942
## [162,] -0.645825276 1.49839730
## [163,] 0.233778229 2.00785990
## [164,] -1.200660574 0.65587437
## [165,] 0.936414428 0.88381593
## [166,] -1.413568498 1.28644679
## [167,] -3.865201590 -4.32499255
## [168,] -2.942925668 -7.95358733
## [169,] -4.344287115 0.25540362
## [170,] -1.898791941 1.32746921
## [171,] -2.565207662 0.46769486
## [172,] -2.911097977 1.40308369
## [173,] -3.551461362 3.33418073
## [174,] 0.960033508 -0.27392959
## [175,] -1.636002570 3.62590970
## [176,] -2.201899066 3.30552421
## [177,] -4.881537153 3.90549433
## [178,] -1.459672941 2.38414906
## [179,] -2.911721136 1.98389004
## [180,] -0.226127668 2.09317036
## [181,] 1.832384023 3.22969962
## [182,] -1.224997785 2.60849926
## [183,] -1.982842411 1.70927033
## [184,] 0.207955245 4.50523397
## [185,] -1.363044133 3.63960975
## [186,] 1.332511027 2.00087009
## [187,] -1.000386016 2.44029753
## [188,] -0.014128538 7.56276724
## [189,] 4.481820610 -1.87299732
## [190,] 1.113568772 2.48788261
## [191,] -2.772937296 3.12768841
## [192,] 0.731226194 -1.83927335
## [193,] -3.708222807 7.17962224
## [194,] -1.193278030 7.94327361
## [195,] -4.437682220 -4.27824074
## [196,] -3.193446281 -1.16597922
## [197,] -0.764141723 2.30548265
## [198,] -1.562182712 -0.07418746
## [199,] -3.159556641 -1.89577068
## [200,] -1.198828079 1.40975639
## [201,] -4.890331739 3.36127060
## [202,] 0.186082422 1.59485135
## [203,] -0.562569303 2.78702797
## [204,] -7.592004227 -3.69133874
## [205,] -4.186718566 4.57649282
## [206,] -2.189155256 -1.04421962
## [207,] -1.847541324 0.38399584
## [208,] -0.280496982 -0.91217446
## [209,] 0.155943200 1.23747676
## [210,] 0.486252888 1.27056692
## [211,] -0.279387744 -0.26875735
## [212,] -1.057809991 2.19352022
## [213,] -2.701424327 -0.35733552
## [214,] -2.363729571 -0.51870597
## [215,] -2.018644253 0.73529347
## [216,] -5.506145910 0.94075026
## [217,] -4.289733249 -0.65252402
## [218,] -1.488265003 5.99443830
## [219,] -5.133977447 0.31515243
## [220,] -7.412614129 6.72349026
## [221,] -4.784336822 5.11374650
## [222,] -3.274647942 8.09711794Ugraph(P0.mRSG.dead.min$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 2, Vcex = .3, prune = T, cut = 0.5, main = "miRNASeqGene data (Deceased Patients)\nFDRcutoff at 1-1e-13, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] -0.37990802 -0.593211809
## [2,] -1.11527186 1.637956620
## [3,] -5.07997470 1.210418350
## [4,] 2.47189248 -1.947750370
## [5,] 3.59990995 -4.212193447
## [6,] 1.66159111 -2.679772945
## [7,] 4.18196716 -4.477689119
## [8,] 0.71499615 0.680191997
## [9,] -1.29870726 2.055499192
## [10,] -0.46192156 2.928103602
## [11,] 5.34582220 0.297960466
## [12,] 1.67613746 1.076890041
## [13,] -0.41721288 -1.532773875
## [14,] 0.06525696 -0.538444357
## [15,] -0.33465485 0.096904727
## [16,] -2.45940619 0.606884849
## [17,] 0.23712022 4.572209840
## [18,] 0.73539508 2.073928854
## [19,] 2.04962391 1.775244283
## [20,] -1.30608368 -0.404025633
## [21,] -0.25129336 1.044583429
## [22,] 0.29422801 -0.605443778
## [23,] -1.46837683 -1.039084066
## [24,] -1.00013074 -1.192726336
## [25,] 0.93003441 4.226271112
## [26,] 2.49150114 3.079798091
## [27,] 4.40368813 1.207056675
## [28,] 4.52640041 2.695149845
## [29,] -0.80819229 2.394709111
## [30,] -2.48255290 1.543950335
## [31,] 0.19279516 2.694819688
## [32,] 0.60265733 -0.635026632
## [33,] -0.95215013 1.023559558
## [34,] 0.32075306 0.554039388
## [35,] -1.78481227 -0.166396891
## [36,] -0.07225822 -0.146498788
## [37,] -0.32878372 0.284056048
## [38,] 0.87665564 -2.101852062
## [39,] 0.68100041 -1.573502145
## [40,] -2.88621111 -3.527278937
## [41,] 3.20545169 -0.356635093
## [42,] 1.15621820 -7.267641341
## [43,] 2.84818893 0.422721926
## [44,] 2.85140819 -0.825306334
## [45,] -4.66166562 2.138876762
## [46,] -4.35911530 2.662370096
## [47,] 0.56819836 6.520767934
## [48,] 4.15821621 0.683006965
## [49,] -1.25651529 2.686351141
## [50,] 1.57455439 1.967601565
## [51,] 4.48566874 -5.594096718
## [52,] -0.18296230 -1.220440486
## [53,] 1.70181433 -1.586869924
## [54,] 4.80523260 2.249408885
## [55,] 4.26000580 3.110987270
## [56,] -1.86727913 6.616056721
## [57,] -0.17113972 0.399350614
## [58,] -0.85906823 0.180553779
## [59,] -3.91865288 0.425137648
## [60,] -2.96877871 -1.828148025
## [61,] -0.35718203 -2.349094524
## [62,] -0.79506255 -1.195507620
## [63,] 0.94569528 -3.439657011
## [64,] -3.00347946 0.391600815
## [65,] -0.70285759 4.274215603
## [66,] 0.90052143 -1.049182514
## [67,] 0.63166371 -1.149237340
## [68,] 3.31434256 -1.795311423
## [69,] -0.26399703 -0.799606058
## [70,] 1.37526193 5.249346160
## [71,] 0.43016855 -3.094043273
## [72,] 2.38781538 9.805244658
## [73,] -1.97645505 -2.557986920
## [74,] -1.37113500 -0.101057952
## [75,] -6.83718886 1.181522228
## [76,] -3.40250956 -2.617310131
## [77,] -1.83867925 -1.071185304
## [78,] -2.55530844 -0.879424088
## [79,] -0.12648986 -0.022967726
## [80,] 1.38216390 0.428025235
## [81,] -3.78430524 0.031637150
## [82,] -0.75411060 0.429203923
## [83,] 1.10654939 -0.632950108
## [84,] -0.67661930 0.621631216
## [85,] -0.91033729 -0.167952222
## [86,] 1.33400286 -0.583368712
## [87,] -1.67666425 -0.550836695
## [88,] -6.97268610 2.015361505
## [89,] 0.98040794 0.674768728
## [90,] 4.84272183 -5.767564145
## [91,] 4.62152789 -6.776552580
## [92,] 3.16560940 -2.221270947
## [93,] 2.56915555 -5.496765431
## [94,] 1.70853407 1.333027656
## [95,] 1.24925489 0.030188356
## [96,] 2.78333828 1.446812581
## [97,] -2.03987598 -0.855088087
## [98,] 3.66357834 -0.927337768
## [99,] 0.48581421 -0.002767253
## [100,] -2.46730239 -0.211272078
## [101,] 0.95794559 -4.154622554
## [102,] 1.96488002 2.866462239
## [103,] -0.88140936 1.618144076
## [104,] -3.00734124 -0.165740332
## [105,] -0.61583973 -0.862788100
## [106,] 3.01442343 -1.345528458
## [107,] 0.68591377 1.276971075
## [108,] 1.10155440 2.437549562
## [109,] 1.52619478 0.203072082
## [110,] -0.27961688 0.749585154
## [111,] -4.08934990 -4.048493586
## [112,] 5.45487795 -7.760438662
## [113,] 7.95416072 -7.198059108
## [114,] 7.14296055 -8.127177278
## [115,] 6.55759383 -8.542230374
## [116,] 3.04194356 -5.348609764
## [117,] 0.34697651 0.374578029
## [118,] -2.62289500 0.975657736
## [119,] -2.86256418 -6.249786970
## [120,] 6.65433745 -0.265508070
## [121,] 0.40249045 -0.277306853
## [122,] -1.46296767 0.680154606
## [123,] -1.82213888 0.638970875
## [124,] 0.90432868 0.532681314
## [125,] -4.60922339 3.651021298
## [126,] 0.86650996 -0.398117041
## [127,] -1.24455359 0.227989309
## [128,] 8.27792563 -6.622460683
## [129,] -0.68749801 1.343117445
## [130,] 7.57541409 -7.672691203
## [131,] 6.10343545 -7.214913670
## [132,] 6.50257980 1.288949494
## [133,] 2.13050740 -2.451636545
## [134,] 5.18651149 -5.006035139
## [135,] -0.93372359 -3.448314902
## [136,] 6.33876696 2.142397824
## [137,] 0.21853865 -1.103753822
## [138,] -5.05589898 -0.953305925
## [139,] 0.26496199 -7.272713364
## [140,] 3.15214462 -3.765291481
## [141,] -2.09856679 -0.553885074
## [142,] 2.03634025 -0.398698557
## [143,] 0.91737334 1.375443671
## [144,] 3.25519535 5.437593450
## [145,] 5.33805975 -9.379237791
## [146,] 0.44456570 1.145069251
## [147,] -0.64829833 0.102334945
## [148,] -3.32298425 3.966834161
## [149,] -2.75923771 2.327978562
## [150,] -4.39978652 4.585280052
## [151,] -4.27441801 4.325604519
## [152,] -4.01037756 4.543689257
## [153,] -3.71761263 5.099091316
## [154,] -1.98524125 2.655529494
## [155,] 1.03967645 1.656999109
## [156,] 0.30735377 1.651693122
## [157,] -3.92063759 5.453115816
## [158,] -2.22413959 2.475348765
## [159,] -3.11557243 4.321902414
## [160,] -2.54443383 3.568153618
## [161,] -4.16582319 4.901932285
## [162,] -3.79667911 4.807022832
## [163,] -3.39605010 5.073288008
## [164,] -2.74493917 4.184207087
## [165,] -0.98688094 -1.665709303
## [166,] -0.79491381 -2.602241184
## [167,] -0.11192190 -2.405705783
## [168,] 1.14926601 -2.204936809
## [169,] -0.54437297 -2.752995157
## [170,] 0.89236497 -1.773277836
## [171,] 0.17352807 -2.707936810
## [172,] 0.33614523 -2.339790992
## [173,] 0.69310661 -2.457095798
## [174,] -0.12685726 -1.997698914
## [175,] -0.79300413 -2.075455870
## [176,] -0.53863499 -1.330451352
## [177,] 2.55091604 -3.422497827
## [178,] 7.77879559 -3.923421407
## [179,] 1.32079814 2.146150057
## [180,] 0.07039243 -0.003138772
## [181,] 1.27467952 0.707289591
## [182,] 0.40359344 0.779911466
## [183,] -1.83134300 0.146538115
## [184,] 1.96137526 6.294033144
## [185,] 0.05766490 1.642796310
## [186,] 3.48021391 1.437327239
## [187,] 6.94244469 0.810980700
## [188,] 1.95360622 0.360343422
## [189,] 0.18858812 -1.381069152
## [190,] -3.77669691 -1.301920438
## [191,] -4.16343743 -2.762636752
## [192,] 1.68862016 -0.487487222
## [193,] -1.08487860 -0.718321853
## [194,] 1.79551581 -0.014318879
## [195,] 3.27053611 -0.036421690
## [196,] 2.38897807 4.279757776
## [197,] -0.07051155 1.095353445
## [198,] 3.76515436 5.536834170
## [199,] 1.70218482 7.197926378
## [200,] -2.54524366 -1.495033555
## [201,] 2.35269946 -2.529211076
## [202,] 2.77498498 -4.287298432
## [203,] 0.99000918 0.107535315
## [204,] 4.57119615 -1.996029392
## [205,] 3.68541135 -5.014490565
## [206,] 0.66616670 2.406738512
## [207,] 0.87113036 -0.627121000
## [208,] 1.03434042 -1.123748715
## [209,] -2.28465960 -1.795998371
## [210,] 5.90922435 4.275192428
## [211,] 2.86232988 1.968618484
## [212,] -1.60233253 1.551536350
## [213,] -1.18223184 0.404979473
## [214,] 3.94039885 -3.957684880
## [215,] -1.43491593 1.104009792
## [216,] 2.94120380 0.891818267
## [217,] 0.01206979 2.108630984
## [218,] -5.72574004 -2.244830274
## [219,] -5.95146808 -1.666489315
## [220,] -6.24989792 -3.957203226
## [221,] 1.27517682 -3.787627810
## [222,] -0.55014895 1.706671037
## [223,] -1.56467166 -2.342658672
## [224,] 0.68744676 3.319278825GGM.mRSG.alive.min = as.data.frame(GGMnetworkStats(P0.mRSG.alive.min$sparseParCor, as.table = T))
GGM.mRSG.dead.min = as.data.frame(GGMnetworkStats(P0.mRSG.dead.min$sparseParCor, as.table = T))
GGM.mRSG.alive.min.order = GGM.mRSG.alive.min[order(GGM.mRSG.alive.min$degree, decreasing = T), ]
GGM.mRSG.dead.min.order = GGM.mRSG.dead.min[order(GGM.mRSG.dead.min$degree, decreasing = T), ]
#Output top 5%
GGM.mRSG.alive.min.order[1:round(nrow(GGM.mRSG.alive.min.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos
## hsa-mir-206 49 1418.6012 0.002336449 1.0000000 25 24
## hsa-mir-137 43 1165.2189 0.002277904 0.8946383 21 22
## hsa-mir-216b 43 1184.0986 0.002267574 0.7987536 26 17
## hsa-mir-873 39 1203.4487 0.002283105 0.7455373 21 18
## hsa-mir-122 38 695.1543 0.002164502 0.7128671 22 16
## hsa-mir-383 37 1594.7997 0.002262443 0.7025122 20 17
## hsa-mir-9-3 36 728.9325 0.002212389 0.7430145 14 22
## hsa-mir-1258 34 1815.9385 0.002217295 0.5642652 12 22
## hsa-mir-153-1 33 801.5665 0.002207506 0.5658723 13 20
## hsa-mir-1251 32 392.5218 0.002100840 0.6603423 12 20
## hsa-mir-129-2 32 389.5903 0.002132196 0.6670785 15 17
## hsa-mir-187 32 493.6639 0.002207506 0.7099541 12 20
## hsa-mir-135a-2 31 335.6088 0.002092050 0.6254414 17 14
## hsa-mir-3144 31 673.1821 0.002136752 0.6450929 16 15
## hsa-mir-519a-1 31 396.8300 0.002127660 0.6431020 15 16
## hsa-mir-551b 31 436.1201 0.002127660 0.6626297 16 15
## hsa-mir-670 31 449.2775 0.002087683 0.5687669 13 18
## hsa-mir-218-1 30 880.7681 0.002169197 0.5802376 17 13
## hsa-mir-105-2 29 1239.1713 0.002092050 0.4763291 11 18
## hsa-mir-526b 29 470.4592 0.002145923 0.5942396 10 19
## hsa-mir-1270-1 28 261.7844 0.002145923 0.6528327 16 12
## hsa-mir-489 28 495.8287 0.002132196 0.5647228 12 16
## hsa-mir-548b 27 398.1059 0.002164502 0.5635849 15 12
## hsa-mir-552 27 313.4868 0.002109705 0.5898855 13 14
## mutualInfo variance partialVar
## hsa-mir-206 0.04985656 1.051120 1
## hsa-mir-137 0.04619291 1.047276 1
## hsa-mir-216b 0.03741822 1.038127 1
## hsa-mir-873 0.03962409 1.040420 1
## hsa-mir-122 0.04052999 1.041363 1
## hsa-mir-383 0.04064633 1.041484 1
## hsa-mir-9-3 0.03922892 1.040009 1
## hsa-mir-1258 0.03059035 1.031063 1
## hsa-mir-153-1 0.04363787 1.044604 1
## hsa-mir-1251 0.04218980 1.043092 1
## hsa-mir-129-2 0.02582477 1.026161 1
## hsa-mir-187 0.02790298 1.028296 1
## hsa-mir-135a-2 0.03420975 1.034802 1
## hsa-mir-3144 0.02832720 1.028732 1
## hsa-mir-519a-1 0.03010324 1.030561 1
## hsa-mir-551b 0.03188718 1.032401 1
## hsa-mir-670 0.02609226 1.026436 1
## hsa-mir-218-1 0.03127445 1.031769 1
## hsa-mir-105-2 0.03090548 1.031388 1
## hsa-mir-526b 0.02804850 1.028446 1
## hsa-mir-1270-1 0.02395249 1.024242 1
## hsa-mir-489 0.03185701 1.032370 1
## hsa-mir-548b 0.02280188 1.023064 1
## hsa-mir-552 0.02455820 1.024862 1GGM.mRSG.dead.min.order[1:round(nrow(GGM.mRSG.dead.min.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos
## hsa-mir-499 59 2048.4571 0.002352941 0.9383511 28 31
## hsa-mir-135a-2 54 980.8479 0.002336449 1.0000000 25 29
## hsa-mir-34b 54 1863.7929 0.002398082 0.9225208 27 27
## hsa-mir-122 46 963.1957 0.002267574 0.7693500 26 20
## hsa-mir-3662 46 512.1049 0.002202643 0.8938969 27 19
## hsa-mir-31 44 1223.3786 0.002192982 0.7079726 23 21
## hsa-mir-124-3 43 425.0625 0.002262443 0.8708875 26 17
## hsa-mir-216a 43 1164.1621 0.002293578 0.7490762 21 22
## hsa-mir-383 43 924.8714 0.002247191 0.6978752 26 17
## hsa-mir-3934 41 622.2584 0.002262443 0.7573500 21 20
## hsa-mir-135b 40 398.2131 0.002232143 0.7985608 18 22
## hsa-mir-3690 38 654.6898 0.002232143 0.7037487 20 18
## hsa-mir-1258 37 840.9645 0.002272727 0.6948739 20 17
## hsa-mir-137 37 1689.3834 0.002252252 0.5386194 18 19
## hsa-mir-200a 37 360.3731 0.002164502 0.6872298 16 21
## hsa-mir-206 37 303.2943 0.002123142 0.7436848 12 25
## hsa-mir-548b 37 609.0097 0.002232143 0.6156501 15 22
## hsa-mir-124-2 36 523.4831 0.002222222 0.7269321 20 16
## hsa-mir-1245 35 453.3964 0.002074689 0.5854388 19 16
## hsa-mir-3923 35 816.0978 0.002192982 0.6554986 17 18
## hsa-mir-124-1 34 592.4080 0.002092050 0.6223953 17 17
## hsa-mir-1269 32 214.9781 0.002197802 0.7095134 16 16
## hsa-mir-216b 32 261.3395 0.002123142 0.6741535 14 18
## hsa-mir-3144 32 304.4228 0.002141328 0.5792189 19 13
## mutualInfo variance partialVar
## hsa-mir-499 0.05698069 1.058635 1
## hsa-mir-135a-2 0.04601150 1.047086 1
## hsa-mir-34b 0.05088074 1.052197 1
## hsa-mir-122 0.03886083 1.039626 1
## hsa-mir-3662 0.03896512 1.039734 1
## hsa-mir-31 0.03849026 1.039241 1
## hsa-mir-124-3 0.03916181 1.039939 1
## hsa-mir-216a 0.03348094 1.034048 1
## hsa-mir-383 0.03646728 1.037140 1
## hsa-mir-3934 0.03847133 1.039221 1
## hsa-mir-135b 0.02800993 1.028406 1
## hsa-mir-3690 0.03196679 1.032483 1
## hsa-mir-1258 0.03038982 1.030856 1
## hsa-mir-137 0.02786437 1.028256 1
## hsa-mir-200a 0.03548027 1.036117 1
## hsa-mir-206 0.03300164 1.033552 1
## hsa-mir-548b 0.02934748 1.029782 1
## hsa-mir-124-2 0.03358706 1.034157 1
## hsa-mir-1245 0.02601377 1.026355 1
## hsa-mir-3923 0.02664374 1.027002 1
## hsa-mir-124-1 0.03165019 1.032156 1
## hsa-mir-1269 0.02672720 1.027088 1
## hsa-mir-216b 0.02458369 1.024888 1
## hsa-mir-3144 0.02583671 1.026173 1ggplot(GGM.mRSG.alive.min.order, aes(x = reorder(rownames(GGM.mRSG.alive.min.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.mRSG.alive.min.order$degree), linetype = "dashed", color = "red") +
# 24th unit: top 5%
geom_hline(yintercept = GGM.mRSG.alive.min.order[24,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "miRNASeqGene", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-13, Surviving Patients")
ggplot(GGM.mRSG.dead.min.order, aes(x = reorder(rownames(GGM.mRSG.dead.min.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.mRSG.dead.min.order$degree), linetype = "dashed", color = "red") +
# 24th unit: top 5%
geom_hline(yintercept = GGM.mRSG.dead.min.order[24,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "miRNASeqGene", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-13, Deceased Patients")
FDRcut 1-1e-6:
P0.mRSG.alive.6 = sparsify(opt.mRSG.alive$optPrec, threshold = "localFDR", FDRcut=1-1e-6)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 3648
## - Corresponding to 3.31 % of possible edges
## P0.mRSG.dead.6 = sparsify(opt.mRSG.dead$optPrec, threshold = "localFDR", FDRcut=1-1e-6)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 4048
## - Corresponding to 3.67 % of possible edges
## set.seed(42)
Ugraph(P0.mRSG.alive.6$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 2, Vcex = .3, prune = T, cut = 0.5, main = "miRNASeqGene data (Surviving Patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] -4.410883321 1.575890771
## [2,] 2.335809123 1.644717176
## [3,] 0.591960134 -2.445122047
## [4,] -1.489250356 -3.088565812
## [5,] -2.915362335 5.295975529
## [6,] 0.018828042 -0.165810387
## [7,] -1.668313876 -5.126466482
## [8,] -1.837556477 -4.683897353
## [9,] 0.046519743 -4.573038654
## [10,] -2.056168783 -4.495283720
## [11,] -0.314511906 -0.802435496
## [12,] -1.663560317 0.824340517
## [13,] 0.415990240 -1.598210427
## [14,] -1.394281427 -2.882330517
## [15,] -1.299759199 -1.479195299
## [16,] -1.312404818 -1.873965129
## [17,] -0.977117451 -1.799296953
## [18,] -1.180747765 -2.141321710
## [19,] -4.397224014 -1.031044847
## [20,] 1.214705864 -3.181072033
## [21,] -0.845671343 -2.297763020
## [22,] -3.863637750 -3.692389412
## [23,] -1.787922426 -2.066947818
## [24,] 1.415181086 -1.078561361
## [25,] -2.272174067 2.172629156
## [26,] -1.679542802 1.481685267
## [27,] 0.028615243 -0.958979603
## [28,] -5.982452441 -2.467038117
## [29,] -0.317161813 -1.023350921
## [30,] -4.711891834 -8.066507645
## [31,] -1.049017302 -1.542003183
## [32,] -1.848478404 -2.853918121
## [33,] -1.290677767 0.399968962
## [34,] -1.235513618 -0.008088149
## [35,] -4.774393731 0.652836006
## [36,] -2.129528367 -1.106231639
## [37,] -1.704410659 -1.328018499
## [38,] -1.526511835 -2.480762671
## [39,] 0.624411104 -0.745770339
## [40,] -0.593119680 -1.825318871
## [41,] 0.156934268 -1.730456735
## [42,] 1.023015595 3.457763011
## [43,] -1.898888780 -1.840375006
## [44,] -4.126708819 -8.017995273
## [45,] -1.409319362 0.231825795
## [46,] -1.783167127 -1.586186977
## [47,] 0.643130475 -1.016550321
## [48,] -1.474483013 -7.510576898
## [49,] -1.155727730 -1.039563615
## [50,] -0.435406423 -1.642519009
## [51,] -1.230840009 -2.601415346
## [52,] 2.425190826 -1.747281813
## [53,] 2.022601999 0.091864034
## [54,] -0.012648802 5.280941633
## [55,] 4.226987398 1.984498915
## [56,] 4.884974247 1.156852365
## [57,] -4.158247686 1.368235704
## [58,] -0.787697905 -2.701625771
## [59,] -0.096300070 -3.078783100
## [60,] -1.678908659 -6.727704311
## [61,] -6.861813663 -6.010468121
## [62,] 5.974604845 -5.077265885
## [63,] 1.030437241 0.229381328
## [64,] 0.064431444 0.543478118
## [65,] 0.512839045 -1.844812106
## [66,] -1.464072995 -2.135059820
## [67,] -5.610727884 3.148020138
## [68,] -0.280719526 -3.209922716
## [69,] 0.152441843 -0.667507131
## [70,] 5.017411754 1.972048397
## [71,] 3.024989617 1.061377749
## [72,] 2.840239244 1.411534460
## [73,] 1.871775132 -2.851423754
## [74,] -1.794552039 -0.060650747
## [75,] -1.359958863 -0.302257701
## [76,] 0.578979397 1.246044934
## [77,] 4.121262936 -6.200403617
## [78,] 0.112883641 -1.200777477
## [79,] -0.092283668 -0.659604450
## [80,] -0.518086431 2.403771667
## [81,] 0.147284252 0.050280217
## [82,] -0.586845368 -2.287341333
## [83,] -0.549335233 -2.417103077
## [84,] 2.968700540 -3.231405341
## [85,] 1.187665736 -1.933943693
## [86,] -0.313243399 3.843220004
## [87,] -1.617116437 -1.749040049
## [88,] -0.356000758 -1.851725814
## [89,] -2.771385449 0.248924187
## [90,] 0.956021208 -0.870213031
## [91,] 3.704961513 -2.769025619
## [92,] -3.287487268 0.691757448
## [93,] 1.367915524 -3.753348500
## [94,] -0.404222573 -0.677708554
## [95,] -2.628234596 -8.147109913
## [96,] 0.666345296 -3.158733060
## [97,] 0.294585550 1.487306704
## [98,] -3.746025574 -2.514182790
## [99,] 0.047161567 -2.107559891
## [100,] 1.637851373 -0.809673739
## [101,] -1.252444440 -1.341469856
## [102,] 0.716679124 -1.357676237
## [103,] 0.835233128 -0.314140892
## [104,] -0.742176081 -3.243965090
## [105,] 0.282714293 -2.614472083
## [106,] -0.736216004 -0.415007759
## [107,] -2.422508904 -1.025411931
## [108,] -1.438157032 -1.447334811
## [109,] 0.117134680 -3.285461049
## [110,] -1.091163696 -2.231508979
## [111,] -4.887688811 -2.615459604
## [112,] -0.448915993 -3.143431244
## [113,] -0.197722782 -2.419963727
## [114,] -2.369663685 -6.576384039
## [115,] -2.910961256 -6.220556071
## [116,] 1.774412146 -4.781130398
## [117,] -2.112672626 -5.130001913
## [118,] -1.254956771 -4.534439825
## [119,] 1.648170864 2.378697666
## [120,] -7.541990659 0.301785911
## [121,] -1.186587779 -0.643912601
## [122,] 0.966339129 -1.737774583
## [123,] 2.896453576 -0.474965784
## [124,] 0.532757347 -2.669035175
## [125,] -0.584605858 -2.983460856
## [126,] -0.494878660 -2.095808631
## [127,] 6.419205404 1.262017536
## [128,] 1.044319149 -1.997908760
## [129,] 0.914374304 0.520646191
## [130,] 0.518028013 -4.036117121
## [131,] -1.631312056 -1.145501924
## [132,] -2.031222754 -2.166930049
## [133,] -1.364297871 -0.992578158
## [134,] -0.489157656 -2.636289844
## [135,] -0.048702709 -1.808417324
## [136,] -1.257625569 -3.048733044
## [137,] 0.023376565 -1.683726165
## [138,] 1.302730212 -9.682032310
## [139,] -0.223284057 -1.995209906
## [140,] -3.020928565 -7.035526352
## [141,] -0.376626826 -1.409632899
## [142,] -5.241578477 -0.699286533
## [143,] -0.648501239 -5.723321914
## [144,] -2.303581394 -4.032290700
## [145,] -0.925664359 -7.358354438
## [146,] -2.486999056 -9.340951045
## [147,] -3.646313151 -4.417066853
## [148,] -1.337974474 -8.639685975
## [149,] -2.728174458 -4.333499074
## [150,] -1.896500647 -8.122664460
## [151,] -4.731306142 -6.692854250
## [152,] 0.728307313 -1.689116673
## [153,] -3.291973009 0.100443887
## [154,] 1.819588216 4.894978725
## [155,] -0.944008479 -1.987205566
## [156,] 2.382959109 -3.649334092
## [157,] 0.382620882 -1.071924599
## [158,] 1.009418179 1.297912043
## [159,] 1.077164409 -1.255289166
## [160,] 1.872414898 -2.334490697
## [161,] -1.949216327 -0.926125583
## [162,] 0.139925363 -2.212225086
## [163,] -2.519093963 -7.746637998
## [164,] -3.873351847 -7.140992973
## [165,] -2.350543754 -5.342256561
## [166,] -0.681510578 -1.985570812
## [167,] -5.058532526 -5.090354125
## [168,] -4.260944809 -6.625325469
## [169,] -2.152362754 0.023001289
## [170,] -4.078465593 -5.046213648
## [171,] 0.529051339 5.772529383
## [172,] -3.562032809 2.207608676
## [173,] 5.449755088 -5.932405566
## [174,] 0.258881337 -0.861075609
## [175,] 1.558190676 -1.529528768
## [176,] -3.196025454 -6.117098150
## [177,] 1.135069674 5.655357464
## [178,] -3.067658817 -5.342132342
## [179,] -2.525644824 -6.905679491
## [180,] -1.710317322 -1.686871995
## [181,] 0.770053246 -1.115532786
## [182,] -0.889466565 -1.115339770
## [183,] -6.411060931 -4.071315556
## [184,] -3.452797741 -5.670169892
## [185,] -1.984916938 -7.236165605
## [186,] -3.948842062 -6.772132548
## [187,] 3.286024847 -4.431134103
## [188,] -0.871478025 -2.966618795
## [189,] 2.398014797 3.130548679
## [190,] -0.457917743 -1.093790648
## [191,] 4.874403628 -0.823545321
## [192,] 3.149587347 -1.574723880
## [193,] 4.943793524 -1.118288685
## [194,] 4.931058238 -1.823442414
## [195,] 4.853772496 -1.482084579
## [196,] 5.075907919 -1.389939855
## [197,] 3.004265753 -1.356291136
## [198,] 3.944404100 0.626683195
## [199,] 0.215191629 0.844721929
## [200,] 3.108484585 -1.202179660
## [201,] 3.253644446 -1.282548041
## [202,] 4.847867693 -0.567656090
## [203,] 4.456639455 -1.554325222
## [204,] 4.373812357 -0.793421824
## [205,] 4.439442976 -1.122033048
## [206,] 4.493242341 -1.808078100
## [207,] 3.146301940 -1.777766781
## [208,] -2.576945671 -1.654570810
## [209,] -2.338601342 -1.976677896
## [210,] -2.494241675 -2.256383464
## [211,] -1.193917391 -1.647399828
## [212,] -1.047698311 -3.041849850
## [213,] -0.280649691 -2.672057043
## [214,] -1.523627975 -2.702669362
## [215,] -1.979321058 -1.307195674
## [216,] -2.295173088 -1.500246618
## [217,] -2.360214573 -2.754907539
## [218,] -2.929545887 -2.748930195
## [219,] -1.040939331 -2.460273904
## [220,] -3.315826238 -7.724123688
## [221,] -3.102881269 -4.073214802
## [222,] -4.464880419 -6.124423029
## [223,] 0.488248853 -3.528164692
## [224,] -6.136323236 -1.357393703
## [225,] -0.616482742 -1.109353344
## [226,] -1.131395516 -2.744216017
## [227,] -0.080778885 -1.120145770
## [228,] -2.495087822 -0.562719539
## [229,] -0.409583496 0.169814004
## [230,] -5.013497076 -1.239710036
## [231,] -0.201000349 -1.675653209
## [232,] -0.659776150 -1.217635517
## [233,] -2.093712231 -0.395424586
## [234,] -1.052911698 -1.310619302
## [235,] -0.126442654 -2.182047178
## [236,] -0.865611754 -0.708255088
## [237,] -1.624120721 -2.291762155
## [238,] 4.322357637 3.640571295
## [239,] -1.795067718 -2.380295850
## [240,] -0.884916967 -2.583485674
## [241,] -4.676290859 3.935273362
## [242,] 1.234198139 -2.206649881
## [243,] -1.344275446 -0.781834221
## [244,] -1.636640083 -0.044271607
## [245,] -0.407213638 -2.178395768
## [246,] -3.276562121 1.480295698
## [247,] 1.620761102 0.825794715
## [248,] 0.381681629 -0.305780699
## [249,] -2.070331379 -1.748017500
## [250,] -0.826346481 0.576633297
## [251,] -0.109201830 1.004898660
## [252,] 1.512920162 0.035661241
## [253,] -1.704500974 -9.359557023
## [254,] -2.687025086 -5.136098585
## [255,] -1.373852211 -4.768538012
## [256,] 0.615020400 -2.045108778
## [257,] -0.648733122 -4.223630401
## [258,] -2.630604850 -4.631170305
## [259,] -0.681233989 -1.600154955
## [260,] -2.623771908 -1.225437998
## [261,] -0.053342742 -1.448642437
## [262,] -0.507464135 -0.269503764
## [263,] 2.444482445 -4.447521842
## [264,] -3.737967911 -8.246254767
## [265,] -3.091057488 -1.674329033
## [266,] -0.534998523 -0.440083312
## [267,] -0.004804377 -2.611609816
## [268,] -1.796875108 -4.168109507
## [269,] -1.022089025 -0.375944870
## [270,] -0.917561681 -0.468124895
## [271,] -1.719139179 -0.776545153
## [272,] -3.445036893 -7.131718064
## [273,] -0.869957808 -0.996888749
## [274,] -2.244719713 -2.463093322
## [275,] -2.086957126 -2.721425423
## [276,] -1.376203399 -2.234149195
## [277,] 2.725104826 -2.820393516
## [278,] -0.697821035 -3.655634478
## [279,] 0.887098216 -2.872899438
## [280,] -3.046523696 -2.014385259
## [281,] 0.379640414 -3.792495488
## [282,] -0.916271443 1.142037047
## [283,] -0.484373014 0.967978088
## [284,] -2.444900753 3.071073093Ugraph(P0.mRSG.dead.6$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 2, Vcex = .3, prune = T, cut = 0.5, main = "miRNASeqGene data (Deceased Patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] 1.281236418 5.106468072
## [2,] -6.478105951 2.156015776
## [3,] -6.275597006 -8.518184316
## [4,] -3.014133446 1.039861088
## [5,] -0.611527829 -2.731583729
## [6,] -0.732168798 -3.672453237
## [7,] 1.340166125 2.361472052
## [8,] -1.991866649 -3.873557757
## [9,] 1.428444088 -2.254433332
## [10,] 3.224289437 -1.936666095
## [11,] 1.121340150 -1.325353626
## [12,] 2.153955350 -1.957202462
## [13,] -1.376813123 -3.197361564
## [14,] -0.127915632 -3.088871567
## [15,] -2.042434749 -1.408432552
## [16,] -2.393752403 -5.207143010
## [17,] -2.534858234 -4.766402741
## [18,] -2.390092852 -2.183436105
## [19,] -1.888887915 -2.050897725
## [20,] -1.409484636 -2.563546566
## [21,] -1.240036946 -1.937070898
## [22,] -0.207856680 -2.791011977
## [23,] -2.893408068 -0.330558034
## [24,] -2.654727270 -1.459732684
## [25,] -1.265352183 -3.456141666
## [26,] -0.990909193 -1.282256021
## [27,] -1.750973184 -2.328082156
## [28,] -1.239732468 3.362150205
## [29,] 0.405720725 5.352233054
## [30,] -0.700214324 -2.376334075
## [31,] 5.901657906 -1.407372708
## [32,] -1.496205672 -0.861666885
## [33,] -1.086278616 -1.542826885
## [34,] -1.284479442 -4.526485891
## [35,] -8.139021031 0.165509817
## [36,] -4.459206231 -3.341462717
## [37,] -3.875792489 -1.399798819
## [38,] -4.330123960 -2.171704407
## [39,] -3.021033676 -1.448238154
## [40,] -1.686889853 -0.709593311
## [41,] -3.126170054 -2.309302683
## [42,] -0.283898698 -3.497920037
## [43,] -2.542309311 -2.514492868
## [44,] -1.017084663 -2.358038903
## [45,] 3.730458456 -3.447570187
## [46,] -0.559640415 -3.404401604
## [47,] -1.730409577 -1.915640125
## [48,] -2.291364432 -2.051317363
## [49,] 5.820122117 -2.029189569
## [50,] -0.053743237 -2.492731629
## [51,] 0.169721016 -2.652958317
## [52,] 0.386844165 -3.581957367
## [53,] -0.311608241 -0.784950831
## [54,] -0.262104062 1.600376287
## [55,] -3.499298480 0.178234922
## [56,] 2.687478688 -6.241841279
## [57,] -4.767795160 -2.035341562
## [58,] -4.423002058 -9.434536343
## [59,] -1.301153432 -1.150387548
## [60,] -0.410958656 -1.328769618
## [61,] 5.678408720 -2.513636087
## [62,] -4.090689054 -3.250133080
## [63,] -4.997035436 -3.474410308
## [64,] -0.676328383 -6.215106364
## [65,] -1.859596884 -6.042117785
## [66,] -2.923730935 -1.128015973
## [67,] -1.084049103 -3.943575606
## [68,] 3.232935499 -5.487081617
## [69,] -0.429720178 -1.732300412
## [70,] -1.332772515 -3.629615396
## [71,] -1.287572761 -9.463462343
## [72,] -4.280248532 -2.656010371
## [73,] -3.980410606 -2.642889063
## [74,] -5.895268776 -4.506705033
## [75,] -5.230061290 -5.600258670
## [76,] -5.578178762 -5.094846798
## [77,] -1.988584928 -2.572052877
## [78,] -2.053609935 -1.985463183
## [79,] -1.489898433 -3.562357208
## [80,] 0.039040015 -1.265056319
## [81,] -1.849447038 -6.560314040
## [82,] 0.780131993 -3.262010724
## [83,] -0.034635430 -1.718216989
## [84,] -7.171005677 -6.640298838
## [85,] 0.484053725 -2.907343955
## [86,] 1.760857353 4.255979196
## [87,] -0.717254988 -4.282206582
## [88,] 0.880230129 -2.302948754
## [89,] -6.253383706 -2.106107623
## [90,] -0.674238523 -3.081518064
## [91,] -0.367164252 -3.020389706
## [92,] 0.234409613 -0.420993683
## [93,] -1.417386180 -1.633906940
## [94,] -2.498927695 1.406197358
## [95,] -1.071029411 -5.222423710
## [96,] -4.195104725 -7.488946652
## [97,] 0.513453877 -1.410471726
## [98,] -1.300621284 -5.633451481
## [99,] -1.773199135 -0.298638319
## [100,] -0.869363382 -1.936304682
## [101,] 4.917067828 -3.314341789
## [102,] -2.854531073 -8.037852725
## [103,] -3.504079886 -7.784745787
## [104,] -3.769042204 -5.184306211
## [105,] -1.402804994 4.822142775
## [106,] -6.907341185 -7.839535367
## [107,] -0.103770025 -0.965126338
## [108,] -0.566097033 -2.500272079
## [109,] -1.982178253 -4.340208171
## [110,] -1.197829305 -1.609677054
## [111,] -1.925089902 -3.040016886
## [112,] -0.274436451 -0.518030546
## [113,] -0.763955467 -2.143048885
## [114,] -0.735196388 -0.565905690
## [115,] -1.491638531 -2.257742781
## [116,] -1.335180009 -1.834426199
## [117,] -0.915029487 -3.310673864
## [118,] -0.807612098 -1.678688794
## [119,] 2.708111617 -8.408004766
## [120,] -1.590131339 -4.728751906
## [121,] -1.756208101 -1.702809204
## [122,] 3.718073484 -1.090197225
## [123,] 2.796696408 -0.944720078
## [124,] 1.798689848 -1.800915826
## [125,] 1.786463742 -0.993869588
## [126,] -0.940717704 -7.354279315
## [127,] -6.570707588 0.534853867
## [128,] 3.115920493 -7.873670726
## [129,] -2.636852378 -2.845152626
## [130,] -0.836580749 -1.255090282
## [131,] -1.491333614 0.295457785
## [132,] -0.490475597 4.641088781
## [133,] -6.275854505 -0.124934158
## [134,] -3.187564493 -2.622873885
## [135,] -2.960487817 -2.906721355
## [136,] -0.239123698 -3.787071880
## [137,] -1.796671723 -4.267733526
## [138,] 0.665968890 -7.204905336
## [139,] -2.513521787 -0.060702582
## [140,] -2.446344705 -4.303520452
## [141,] -0.763631087 -3.996999026
## [142,] -1.972276297 -2.763089542
## [143,] -2.039952888 -2.273471359
## [144,] -2.518988985 2.359531683
## [145,] -4.813918905 -7.334903084
## [146,] 0.140893478 -3.824199495
## [147,] -2.595921128 -1.745084143
## [148,] -0.571586410 -4.046408584
## [149,] 4.941458323 -1.690796162
## [150,] -2.606236218 -3.048412355
## [151,] 4.542867173 -1.822108653
## [152,] -0.983262345 -2.050266247
## [153,] 0.840993342 0.987864008
## [154,] 4.228563715 -1.426143650
## [155,] 2.300634630 -0.666161855
## [156,] 3.494340674 -0.387266102
## [157,] 3.524025658 0.069473391
## [158,] 5.130482138 -2.009042192
## [159,] -5.997651075 -3.681026041
## [160,] 4.046579393 -3.356954190
## [161,] 2.032401003 -1.447083748
## [162,] 2.799678102 0.557562916
## [163,] 4.482518490 -0.528382613
## [164,] -1.777040850 -2.692331570
## [165,] -1.189050319 -2.497636889
## [166,] 0.594684322 -4.914614497
## [167,] 0.604748752 4.536581516
## [168,] -3.890604441 -4.588571097
## [169,] -1.292937505 -2.201387844
## [170,] -2.465356657 -3.675274174
## [171,] -0.737774819 -2.905232945
## [172,] -1.506464500 -3.209235402
## [173,] -3.051522892 -3.782448816
## [174,] -1.378976886 -2.051676878
## [175,] -3.296473971 -2.949265937
## [176,] 4.432211991 -3.059583783
## [177,] 4.166026394 -2.182495267
## [178,] 5.304195771 -1.333193300
## [179,] 1.754915563 0.005813276
## [180,] -8.212312920 -1.195945422
## [181,] -2.520257428 -2.306386595
## [182,] 3.879662917 -2.682853096
## [183,] 3.827440972 -2.366999924
## [184,] 1.961600243 2.113562006
## [185,] 0.891796704 -1.781589435
## [186,] 1.177696200 -5.000580010
## [187,] 0.577873799 -0.820072419
## [188,] -4.179737904 -3.629812773
## [189,] -0.115230711 -2.294600194
## [190,] -2.813616584 -4.484625895
## [191,] 4.783080130 -2.214439428
## [192,] -0.437606166 0.522921007
## [193,] 2.609792973 -2.136544991
## [194,] 5.045677769 -2.650378619
## [195,] -1.026311132 -3.738922761
## [196,] -2.015629368 -2.443574560
## [197,] -1.691614530 -2.822038112
## [198,] 2.378304931 1.396396947
## [199,] 4.997737872 -1.186512418
## [200,] 0.588959581 -5.769720233
## [201,] 3.898401641 -1.647820762
## [202,] -2.152196546 -3.007796746
## [203,] -5.702742240 -8.065562665
## [204,] -7.868116974 -5.295119068
## [205,] -3.733276270 3.736794822
## [206,] -2.136142982 -1.763692295
## [207,] -3.250810152 -0.702632399
## [208,] -2.674630188 -2.002712295
## [209,] -4.237012264 -0.179146291
## [210,] -4.271704805 -0.597891399
## [211,] -3.894191804 -0.409637170
## [212,] -3.905195753 -0.132374867
## [213,] -2.488131284 -1.390038905
## [214,] -2.435531385 -2.822973618
## [215,] -2.958133544 -2.267126190
## [216,] -4.770280594 -0.190443349
## [217,] -2.283440250 -0.992250525
## [218,] -3.926677857 -1.015974304
## [219,] -3.550439906 -0.878367241
## [220,] -4.319698013 -1.143570867
## [221,] -4.564034288 -1.191416668
## [222,] -4.752691409 -0.914579213
## [223,] -3.313498854 -0.430458476
## [224,] -0.919944155 -2.768806988
## [225,] -0.006442715 -3.524269899
## [226,] 0.445706119 -3.237776502
## [227,] 0.484399325 -2.476188055
## [228,] 0.139709396 -3.350348750
## [229,] -0.395489721 -2.453663240
## [230,] 0.435173133 -2.170610106
## [231,] 0.152956079 -2.007080894
## [232,] 0.582098670 -2.692861295
## [233,] -0.264274704 -1.910613387
## [234,] -1.070670209 -2.974383168
## [235,] -0.269808917 -2.255863198
## [236,] 4.749720232 -0.983485269
## [237,] 1.764982932 -2.429642539
## [238,] 4.226706458 -0.759273579
## [239,] -2.940034206 -1.821094023
## [240,] -1.607274211 -1.786388999
## [241,] -2.051463996 -3.158804470
## [242,] -1.777741184 -3.636534144
## [243,] -2.774902268 -3.178140123
## [244,] -3.066189665 -4.656366870
## [245,] -2.209115714 -3.778960210
## [246,] -3.423451002 -4.492136474
## [247,] -3.133266985 -5.101465995
## [248,] -1.799952477 -3.091962473
## [249,] -0.902294808 -2.999860526
## [250,] -2.794087450 -3.981702417
## [251,] -3.482825317 -3.540849343
## [252,] -1.284158273 -0.073873357
## [253,] -2.565327112 4.975166381
## [254,] -1.788659158 -1.418481960
## [255,] -1.198270041 -2.729332300
## [256,] -1.119602298 -0.967577294
## [257,] -0.931628685 -4.942092635
## [258,] -4.487856017 -4.826665687
## [259,] -2.336797048 -2.357847858
## [260,] -4.302388030 -4.157214738
## [261,] -8.714075433 -3.410142518
## [262,] -1.916482777 -5.515516856
## [263,] -2.434464357 -3.348664719
## [264,] 3.992132830 2.984633042
## [265,] -5.990197538 2.652841304
## [266,] -1.103994137 1.750778390
## [267,] 5.576208175 -0.472309842
## [268,] 4.602446199 -2.816606187
## [269,] 1.696885920 -2.254014792
## [270,] -0.607982630 -0.314318833
## [271,] -0.385922804 -2.699039133
## [272,] -0.921477055 -0.257947859
## [273,] 2.746191046 -1.973818970
## [274,] -1.281717607 -4.051686399
## [275,] -0.557282797 -1.970509863
## [276,] -0.722446870 -3.221349529
## [277,] -1.635133906 -3.937724710
## [278,] -4.076137380 0.817756692
## [279,] 4.097682324 -0.260194914
## [280,] -2.190904363 -0.488484274
## [281,] -1.445675198 -4.250167626
## [282,] -1.280286551 -3.042256313
## [283,] 3.251371797 -1.595884741
## [284,] -3.411395580 -1.880547486
## [285,] -2.074012374 -1.048844773
## [286,] -1.763275223 -3.393683940
## [287,] -8.031295007 -5.947837585
## [288,] 4.552575074 -2.467472528
## [289,] 0.726705272 -4.141446091
## [290,] -0.633027134 -4.603009199
## [291,] -0.259343888 -4.438026599
## [292,] 0.179184260 -4.812754645
## [293,] -4.726005771 1.494911534
## [294,] -1.553497203 -1.140751536
## [295,] -2.220543169 -1.580859432
## [296,] -1.130912306 -0.703701031
## [297,] -0.445929915 -4.860896210
## [298,] -8.533611002 -4.595137311
## [299,] -0.757860775 -6.771858128
## [300,] -0.521013782 5.471258471GGM.mRSG.alive.6 = as.data.frame(GGMnetworkStats(P0.mRSG.alive.6$sparseParCor, as.table = T))
GGM.mRSG.dead.6 = as.data.frame(GGMnetworkStats(P0.mRSG.dead.6$sparseParCor, as.table = T))
GGM.mRSG.alive.6.order = GGM.mRSG.alive.6[order(GGM.mRSG.alive.6$degree, decreasing = T), ]
GGM.mRSG.dead.6.order = GGM.mRSG.dead.6[order(GGM.mRSG.dead.6$degree, decreasing = T), ]
#Output top 5%
GGM.mRSG.alive.6.order[1:round(nrow(GGM.mRSG.alive.6.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos
## hsa-mir-137 87 1837.7076 0.001992032 1.0000000 48 39
## hsa-mir-383 84 1992.8389 0.002004008 0.9541137 44 40
## hsa-mir-218-1 76 1669.6722 0.001923077 0.7818418 44 32
## hsa-mir-206 74 1012.6948 0.001984127 0.8844179 39 35
## hsa-mir-216b 74 1225.0672 0.001980198 0.8412584 46 28
## hsa-mir-873 73 1313.1597 0.001915709 0.8216630 38 35
## hsa-mir-122 67 1451.5418 0.001912046 0.7415744 33 34
## hsa-mir-489 67 1111.2468 0.001915709 0.8137188 28 39
## hsa-mir-1251 65 395.6778 0.001919386 0.8774891 29 36
## hsa-mir-488 64 1180.6475 0.001926782 0.7163433 34 30
## hsa-mir-135a-2 63 810.8663 0.001923077 0.7851301 33 30
## hsa-mir-329-1 63 1899.6925 0.001811594 0.3850826 15 48
## hsa-mir-1258 61 1211.1747 0.001869159 0.6216818 27 34
## hsa-mir-3144 61 515.1572 0.001883239 0.7482783 30 31
## hsa-mir-3923 60 627.8127 0.001834862 0.7778098 29 31
## hsa-mir-551b 60 403.3193 0.001912046 0.7745075 32 28
## hsa-mir-670 59 382.6725 0.001862197 0.7336644 33 26
## hsa-mir-618 58 365.6687 0.001831502 0.7536765 30 28
## hsa-mir-767 58 482.9527 0.001901141 0.7356146 30 28
## hsa-mir-153-1 57 379.2604 0.001886792 0.6984854 28 29
## hsa-mir-105-1 56 1017.8826 0.001841621 0.6306196 24 32
## hsa-mir-105-2 56 771.4176 0.001845018 0.6404011 26 30
## hsa-mir-1305 56 424.6814 0.001869159 0.6788947 28 28
## hsa-mir-187 56 333.7653 0.001855288 0.7495519 23 33
## mutualInfo variance partialVar
## hsa-mir-137 0.05792767 1.059638 1
## hsa-mir-383 0.05465824 1.056180 1
## hsa-mir-218-1 0.04385892 1.044835 1
## hsa-mir-206 0.05367832 1.055145 1
## hsa-mir-216b 0.04699794 1.048120 1
## hsa-mir-873 0.04789463 1.049060 1
## hsa-mir-122 0.04895314 1.050171 1
## hsa-mir-489 0.04195829 1.042851 1
## hsa-mir-1251 0.04952727 1.050774 1
## hsa-mir-488 0.02966610 1.030111 1
## hsa-mir-135a-2 0.04266945 1.043593 1
## hsa-mir-329-1 0.02971638 1.030162 1
## hsa-mir-1258 0.03681363 1.037500 1
## hsa-mir-3144 0.03689478 1.037584 1
## hsa-mir-3923 0.03681679 1.037503 1
## hsa-mir-551b 0.03823331 1.038974 1
## hsa-mir-670 0.03372321 1.034298 1
## hsa-mir-618 0.03482732 1.035441 1
## hsa-mir-767 0.04426712 1.045262 1
## hsa-mir-153-1 0.04813209 1.049309 1
## hsa-mir-105-1 0.03794908 1.038678 1
## hsa-mir-105-2 0.03916218 1.039939 1
## hsa-mir-1305 0.03149700 1.031998 1
## hsa-mir-187 0.03412042 1.034709 1GGM.mRSG.dead.6.order[1:round(nrow(GGM.mRSG.dead.6.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos
## hsa-mir-499 101 2695.7817 0.001937984 0.9935124 51 50
## hsa-mir-122 95 1935.1052 0.001908397 1.0000000 49 46
## hsa-mir-34b 87 2349.9015 0.001915709 0.9490625 41 46
## hsa-mir-135a-2 85 1112.3558 0.001811594 0.9889366 43 42
## hsa-mir-137 79 2100.1161 0.001862197 0.7916283 38 41
## hsa-mir-383 78 779.5652 0.001811594 0.8295444 50 28
## hsa-mir-3923 76 1658.6989 0.001838235 0.8465114 38 38
## hsa-mir-1258 75 785.0977 0.001763668 0.8618237 41 34
## hsa-mir-3662 75 952.5645 0.001766784 0.8837260 39 36
## hsa-mir-216a 74 1268.1457 0.001865672 0.8135543 38 36
## hsa-mir-31 72 544.9517 0.001792115 0.8325622 40 32
## hsa-mir-124-3 70 598.5971 0.001821494 0.8612900 33 37
## hsa-mir-200a 70 766.1566 0.001828154 0.8150167 32 38
## hsa-mir-1245 68 686.8029 0.001808318 0.8118720 34 34
## hsa-mir-577 68 1076.6221 0.001848429 0.7798470 32 36
## hsa-mir-1197 66 1448.1652 0.001712329 0.4119729 17 49
## hsa-mir-3166 66 585.9955 0.001814882 0.7648800 27 39
## hsa-mir-429 66 763.7550 0.001730104 0.7210737 33 33
## hsa-mir-548b 66 572.3429 0.001811594 0.7223032 27 39
## hsa-mir-346 65 817.6931 0.001733102 0.7394082 28 37
## hsa-mir-3690 65 1083.3959 0.001828154 0.7653713 40 25
## hsa-mir-3934 65 497.7596 0.001831502 0.8112931 32 33
## hsa-mir-124-2 64 801.2065 0.001785714 0.7727001 34 30
## hsa-mir-1269 64 497.1907 0.001818182 0.8355177 27 37
## mutualInfo variance partialVar
## hsa-mir-499 0.06262338 1.064626 1
## hsa-mir-122 0.04895106 1.050169 1
## hsa-mir-34b 0.05565476 1.057233 1
## hsa-mir-135a-2 0.04909062 1.050316 1
## hsa-mir-137 0.03605993 1.036718 1
## hsa-mir-383 0.04222177 1.043126 1
## hsa-mir-3923 0.03482351 1.035437 1
## hsa-mir-1258 0.03729357 1.037998 1
## hsa-mir-3662 0.04284384 1.043775 1
## hsa-mir-216a 0.03886051 1.039625 1
## hsa-mir-31 0.04139258 1.042261 1
## hsa-mir-124-3 0.04291929 1.043854 1
## hsa-mir-200a 0.04055197 1.041385 1
## hsa-mir-1245 0.03116445 1.031655 1
## hsa-mir-577 0.03198705 1.032504 1
## hsa-mir-1197 0.02451625 1.024819 1
## hsa-mir-3166 0.03335668 1.033919 1
## hsa-mir-429 0.03063146 1.031105 1
## hsa-mir-548b 0.03433014 1.034926 1
## hsa-mir-346 0.03234074 1.032869 1
## hsa-mir-3690 0.03605706 1.036715 1
## hsa-mir-3934 0.04097539 1.041826 1
## hsa-mir-124-2 0.03843682 1.039185 1
## hsa-mir-1269 0.03232673 1.032855 1ggplot(GGM.mRSG.alive.6.order, aes(x = reorder(rownames(GGM.mRSG.alive.6.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.mRSG.alive.6.order$degree), linetype = "dashed", color = "red") +
# 24th unit: top 5%
geom_hline(yintercept = GGM.mRSG.alive.6.order[24,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "miRNASeqGene", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-6, Surviving Patients")
ggplot(GGM.mRSG.dead.6.order, aes(x = reorder(rownames(GGM.mRSG.dead.6.order), -degree), y = degree)) +
geom_point() +
geom_hline(yintercept = mean(GGM.mRSG.dead.6.order$degree), linetype = "dashed", color = "red") +
# 24th unit: top 5%
geom_hline(yintercept = GGM.mRSG.dead.6.order[24,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "miRNASeqGene", guide = guide_axis(angle = 90)) +
ggtitle("Variables sorted by degree, FDR = 1-1e-6, Deceased Patients")
D. All Data
data.all.alive = data.numeric[which(data.Y == 0), ]
data.all.dead = data.numeric[which(data.Y == 1), ]set.seed(42)
opt.all.alive = optPenalty.kCVauto(Y = data.all.alive, lambdaMin = 1e-11, lambdaMax = 10)
opt.all.alive$optLambda## [1] 1.148033set.seed(42)
#Warning: NA/Inf replaced by maximum positive value, lambda stuck at 1000
#Same as above, solved by setting lambdaMax = 10 instead of 1000
opt.all.dead = optPenalty.kCVauto(Y = data.all.dead, lambdaMin = 1e-11, lambdaMax = 10)
#opt.all.dead.10 = optPenalty.kCVauto(Y = data.all.dead, lambdaMin = 1e-11, lambdaMax = 1000, fold = 10)
#opt.all.dead.5 = optPenalty.kCVauto(Y = data.all.dead, lambdaMin = 1e-11, lambdaMax = 1000, fold = 5)
#opt.all.dead.3 = optPenalty.kCVauto(Y = data.all.dead, lambdaMin = 1e-11, lambdaMax = 1000, fold = 3)
#setNames(c(3,5,10,43), c(opt.all.dead.3$optLambda, opt.all.dead.5$optLambda, opt.all.dead.10$optLambda, opt.all.dead$optLambda))
opt.all.dead$optLambda## [1] 0.976131edgeHeat(opt.all.alive$optPrec, diag = F, textsize = 1)## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
edgeHeat(opt.all.dead$optPrec, diag = F, textsize = 1)## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
Smallest possible FDRcut:
P0.all.alive.min = sparsify(opt.all.alive$optPrec, threshold = "localFDR", FDRcut=1-1e-14)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 5712
## - Corresponding to 2.33 % of possible edges
## P0.all.dead.min = sparsify(opt.all.dead$optPrec, threshold = "localFDR", FDRcut=1-1e-14)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 6334
## - Corresponding to 2.58 % of possible edges
## PcorP.a.min = pruneMatrix(P0.all.alive.min$sparseParCor)
Colors.a.min <- rownames(PcorP.a.min)
Colors.a.min[grep("hsa", rownames(PcorP.a.min))] <- "red"
Colors.a.min[grep(".RPPA", rownames(PcorP.a.min))] <- "green"
Colors.a.min[grep(".R2Gn", rownames(PcorP.a.min))] <- "cyan"PcorP.d.min = pruneMatrix(P0.all.dead.min$sparseParCor)
Colors.d.min <- rownames(PcorP.d.min)
Colors.d.min[grep("hsa", rownames(PcorP.d.min))] <- "red"
Colors.d.min[grep(".RPPA", rownames(PcorP.d.min))] <- "green"
Colors.d.min[grep(".R2Gn", rownames(PcorP.d.min))] <- "cyan"set.seed(42)
Ugraph(PcorP.a.min, type = "fancy", lay = "layout_with_fr", Vsize = 2, Vcex = .3, prune = T, cut = 0.5,
Vcolor = Colors.a.min,
main = "All Numerical data (Surviving patients)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\nRed: miRNASeqGene; Blue: RNASeq2GeneNorm; Green: RPPA Array")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] 4.94001790 0.04297872
## [2,] 5.28049205 3.54633585
## [3,] -3.51965070 -2.17536767
## [4,] -1.73787362 -0.12166340
## [5,] -1.61049508 0.41007927
## [6,] 1.20421603 8.19201295
## [7,] 3.11587810 -7.51119423
## [8,] 8.56349208 0.73575705
## [9,] 0.13531717 -0.40140813
## [10,] -1.61347896 2.76810081
## [11,] -1.21703648 2.88475750
## [12,] -0.16348880 2.62351063
## [13,] -1.79961063 3.63248300
## [14,] 0.45257270 0.89604357
## [15,] -0.11877857 2.96074323
## [16,] -0.88282326 -0.93416485
## [17,] -4.56601914 -4.36861174
## [18,] -1.34521242 1.07513705
## [19,] -0.80907468 0.99553528
## [20,] -2.04997521 -0.02868829
## [21,] 0.69704766 0.76944843
## [22,] 0.23609348 0.83260082
## [23,] 2.41448394 2.83104672
## [24,] -2.47282376 1.49227880
## [25,] -0.57184425 -0.37164780
## [26,] -3.26326063 0.24182625
## [27,] 1.02925675 -0.40300228
## [28,] -1.96280944 -0.60016656
## [29,] 0.55765130 -4.64331611
## [30,] 1.39413515 -4.07885516
## [31,] -1.80430259 0.64937754
## [32,] 5.68985437 -2.86560998
## [33,] -0.62948745 -0.93487593
## [34,] -6.53406637 4.62060264
## [35,] 0.99150340 0.01308239
## [36,] -0.53403335 1.76926740
## [37,] 0.16077241 -2.47147314
## [38,] 1.50471759 0.50383567
## [39,] 6.14796426 5.95698029
## [40,] -0.23722818 1.74014168
## [41,] -0.10603031 1.29163511
## [42,] -1.27042630 0.83886740
## [43,] 1.57970363 -0.39435362
## [44,] -0.03333145 8.43815653
## [45,] -0.41503001 0.83367757
## [46,] 0.81508623 0.01280806
## [47,] 6.26924432 -0.85203410
## [48,] 0.02330205 1.42596656
## [49,] -5.50103840 5.99429414
## [50,] 1.08231441 -1.93469156
## [51,] -0.19773931 0.38863089
## [52,] 0.32830241 -1.48261713
## [53,] -4.93503350 5.12606887
## [54,] -0.41908109 -0.06642642
## [55,] 0.44651929 0.30192108
## [56,] 0.23770811 1.61567504
## [57,] 5.86344223 -5.00530388
## [58,] 4.28239877 -0.33879027
## [59,] -5.44534102 1.63104711
## [60,] -6.89741173 1.54872735
## [61,] 5.82453197 1.69317344
## [62,] 3.11376642 -3.54189173
## [63,] -0.82790024 0.32807523
## [64,] -1.46014663 -0.09533553
## [65,] -4.04762389 5.24181924
## [66,] -4.35519250 -2.78491557
## [67,] 2.25344901 0.46930386
## [68,] 2.44963182 0.03021716
## [69,] -0.41662175 -1.64121349
## [70,] -0.32077345 0.35835872
## [71,] 2.45794264 -7.75093857
## [72,] -2.29021824 -7.49336650
## [73,] -1.13883107 1.31332498
## [74,] 0.58151277 -0.94692619
## [75,] 4.13300885 -1.60976984
## [76,] 3.84665889 -3.06409740
## [77,] 3.45022238 -3.65271708
## [78,] 0.54742168 4.19469921
## [79,] 2.73149317 -0.77243765
## [80,] 1.13967657 0.66906322
## [81,] 1.94609739 -3.57068766
## [82,] 8.07162868 2.40283078
## [83,] -8.39996434 -1.55956544
## [84,] -8.62326923 -0.84004584
## [85,] 0.23335609 -1.64990478
## [86,] 1.12166185 -0.78671148
## [87,] -2.10045474 -5.50672460
## [88,] -3.37104754 -3.73748000
## [89,] 1.19561181 -1.50000889
## [90,] -0.43081367 1.18133659
## [91,] -0.66896070 0.74922935
## [92,] -0.61631437 8.46991960
## [93,] 2.23973366 -2.63174113
## [94,] -0.69016817 -1.16200845
## [95,] 1.57792983 4.55408633
## [96,] 5.53445143 2.40990786
## [97,] 0.72807527 -0.89983607
## [98,] 0.11471245 0.44797394
## [99,] 2.58749727 -0.51230820
## [100,] 0.69444273 0.41380159
## [101,] 2.33651669 5.79593489
## [102,] 6.46259673 5.48489027
## [103,] 6.86668424 4.97883846
## [104,] 2.53617171 2.20610088
## [105,] 8.18105571 -1.92387205
## [106,] -3.50150549 1.55757040
## [107,] -0.10506843 -0.25162529
## [108,] -6.30600508 5.00029252
## [109,] 1.81880925 -7.93192860
## [110,] -2.35544094 -1.08076441
## [111,] 3.98825537 -0.81408029
## [112,] -4.32660523 -2.09691055
## [113,] -0.56387288 0.06392528
## [114,] 2.03270795 -0.48853285
## [115,] 0.09634740 0.23805374
## [116,] -1.46369755 -0.50184174
## [117,] 2.04183450 -2.11711604
## [118,] -1.24496120 -0.34829220
## [119,] -1.17974296 0.07353099
## [120,] 0.44935770 -1.49961175
## [121,] 0.28989271 -2.20731259
## [122,] -0.38470700 -0.17148676
## [123,] -1.72228564 -0.78838230
## [124,] -0.56896733 1.10801406
## [125,] 8.28886490 1.17895496
## [126,] 0.88398485 0.83286434
## [127,] 0.02741326 -1.44565632
## [128,] -3.17403350 5.10434311
## [129,] -2.97138961 4.53149454
## [130,] 0.98545127 4.89477426
## [131,] -2.56657377 3.56137291
## [132,] -2.12726494 2.02801809
## [133,] -3.53485339 -4.98321389
## [134,] 0.56340026 8.33454960
## [135,] 4.50233362 7.28146842
## [136,] 1.11769909 0.28437854
## [137,] 1.32611202 1.24555401
## [138,] 0.64912762 3.29518500
## [139,] -4.60578221 -7.27078356
## [140,] -1.14406606 -1.28925358
## [141,] -0.37590705 1.72385608
## [142,] -0.27030232 0.69139733
## [143,] -1.46651114 -0.91036371
## [144,] -1.22532094 8.57295863
## [145,] -6.51939336 -5.79432286
## [146,] 3.31885411 0.22632398
## [147,] -1.36409118 -1.78151028
## [148,] 1.13497887 -0.35206298
## [149,] -0.90764516 0.66492218
## [150,] -0.75457600 -0.99228640
## [151,] -0.96942007 -0.21019031
## [152,] -0.36792693 -0.41523301
## [153,] -1.50846971 0.61168739
## [154,] 0.09521578 0.83913345
## [155,] -8.07453943 4.24263426
## [156,] -0.92697918 0.85856076
## [157,] -5.27834437 3.82000478
## [158,] -0.13878192 -0.39626436
## [159,] 3.24114692 -1.78865543
## [160,] -2.55643217 2.34234316
## [161,] -0.95874813 2.81123257
## [162,] -4.13888485 4.56030178
## [163,] -6.50114923 8.75549094
## [164,] -0.27651879 4.33708050
## [165,] -5.85506639 5.71000672
## [166,] -1.02264728 3.27286531
## [167,] -1.88441011 7.02143968
## [168,] -3.83096899 8.19413824
## [169,] -0.27600734 -0.80571691
## [170,] 2.80832487 0.51116778
## [171,] -0.47957642 6.24498824
## [172,] -0.50740491 -0.77826796
## [173,] 3.86883383 -1.19541545
## [174,] -0.55790140 0.27750028
## [175,] 1.93563394 -2.51296403
## [176,] -0.26707266 -1.46858208
## [177,] -0.48510901 -2.84426835
## [178,] -1.00888466 0.26667193
## [179,] -1.37890534 -0.43660482
## [180,] -4.02252815 6.74766226
## [181,] -3.27004761 6.82127270
## [182,] -1.92210427 3.38224968
## [183,] -4.31146157 -3.64810056
## [184,] -0.14806123 0.10063905
## [185,] -3.32632613 7.42148479
## [186,] -4.28805505 7.93268313
## [187,] 1.48718829 -1.22597105
## [188,] -1.84517927 4.60259422
## [189,] 4.91789548 6.92440081
## [190,] -8.63612854 0.50376122
## [191,] 0.44936466 5.88983216
## [192,] -4.53747069 0.94080661
## [193,] 1.91953305 3.91023877
## [194,] -6.33190920 -2.81084642
## [195,] 0.25404109 -1.35565112
## [196,] -1.54173417 -2.50641940
## [197,] -2.30648375 4.90117110
## [198,] -4.23300124 1.72972071
## [199,] -0.75960206 4.28218623
## [200,] -2.96784171 6.17354698
## [201,] 0.10774232 1.16751585
## [202,] 0.78490245 1.02251033
## [203,] -0.08712960 -0.74298804
## [204,] -1.29646534 7.45575018
## [205,] -1.37658250 4.41658443
## [206,] -7.08647578 8.26416232
## [207,] -2.35889434 6.39549889
## [208,] 0.31263153 -4.15084069
## [209,] -0.19824163 -1.29444426
## [210,] 3.62700275 -4.95251902
## [211,] 0.24634333 0.19267033
## [212,] -5.65742239 -0.88413417
## [213,] -4.22445664 -0.60135166
## [214,] -5.53776836 -1.46333249
## [215,] -5.06810120 -0.84723200
## [216,] -4.93740052 -1.23334391
## [217,] -4.79429829 0.21677753
## [218,] -3.28704527 -1.10600682
## [219,] 7.27428652 -2.80461569
## [220,] -2.13569807 -2.30508213
## [221,] -4.50695763 -1.15256438
## [222,] -4.08711131 -0.96740339
## [223,] -5.73822436 -2.28085134
## [224,] -5.31762766 -2.02559589
## [225,] -4.81751926 -0.37785463
## [226,] -4.87298579 -0.10402143
## [227,] -5.17961550 -0.46280805
## [228,] -3.77461233 -0.99066253
## [229,] 1.03425970 1.60746058
## [230,] -0.72376324 1.88921766
## [231,] 0.40727852 2.45822193
## [232,] 0.55674964 1.24709755
## [233,] 0.05966812 1.96549872
## [234,] -0.63630634 1.26825637
## [235,] 0.55122322 1.93183762
## [236,] 0.75877620 0.90431121
## [237,] 1.01793908 1.91025068
## [238,] -0.06859399 2.10468473
## [239,] -0.37413207 2.48836701
## [240,] -0.34334989 1.43959932
## [241,] -5.80474172 9.25472573
## [242,] -2.05644582 2.65141291
## [243,] 5.42238904 6.67553827
## [244,] -2.51826814 6.00976475
## [245,] 0.80949537 2.41991276
## [246,] -3.11213997 -7.29180453
## [247,] -0.80488518 -0.01245419
## [248,] -0.13280413 1.02080521
## [249,] 1.56128955 0.79811509
## [250,] 0.94294937 -1.13878770
## [251,] 2.16514964 -0.15489518
## [252,] 4.30493737 0.71110481
## [253,] 0.09118648 0.03450715
## [254,] 0.59908169 -0.50625002
## [255,] 1.86090447 1.34388255
## [256,] 0.39830157 1.21012695
## [257,] -0.28596095 0.02011224
## [258,] 0.27135478 2.10045337
## [259,] -1.30775800 -0.07610585
## [260,] -2.93932463 -5.67634188
## [261,] -0.11948071 0.58589646
## [262,] -0.85758368 -0.78408513
## [263,] 8.42072059 -0.76502333
## [264,] -2.70581622 0.42671454
## [265,] 0.35587920 -0.81547261
## [266,] 1.41354366 1.50886683
## [267,] 0.31906905 0.37859560
## [268,] 3.04531820 -1.10742471
## [269,] 3.08051543 1.20769191
## [270,] 8.34341768 -1.29386414
## [271,] -1.60981365 0.24695936
## [272,] 0.77480047 -0.28537892
## [273,] 2.02206351 1.61986018
## [274,] -0.89787833 -2.90734715
## [275,] 3.13156102 0.80142098
## [276,] -6.13495846 5.37333031
## [277,] -1.55878084 3.78915005
## [278,] -1.49257524 2.46952097
## [279,] -1.07528242 -0.04138906
## [280,] -0.99411743 1.51857214
## [281,] -1.51163268 3.53986592
## [282,] -0.09608383 0.82647269
## [283,] -0.08247487 -2.72740435
## [284,] -1.29723913 0.45185367
## [285,] 0.41892053 1.45433801
## [286,] -4.96869276 1.08869117
## [287,] -4.90741995 7.57959970
## [288,] -2.51972388 -0.61486406
## [289,] -0.10653068 1.84623972
## [290,] -1.05837147 1.09932191
## [291,] -1.88663635 2.18039645
## [292,] 0.72934286 0.50567093
## [293,] 0.84581194 0.66739022
## [294,] -1.64304397 0.99414980
## [295,] -3.78670573 6.18023242
## [296,] -0.48320386 0.39347785
## [297,] -1.22354809 1.76092060
## [298,] -1.41164101 1.60215667
## [299,] -0.55939305 0.52442610
## [300,] -3.78539436 1.34845287
## [301,] 0.67947663 1.64132959
## [302,] -0.71458279 -2.48912767
## [303,] 1.92963902 1.13426288
## [304,] -1.77698631 -2.56385105
## [305,] 0.95531281 2.60497253
## [306,] 2.20746863 -1.23860739
## [307,] 1.08047637 -4.44754638
## [308,] -0.51240261 -1.79092245
## [309,] 2.53253330 -5.17702319
## [310,] -1.92736017 -7.88506228
## [311,] -0.73160606 -1.66758602
## [312,] 2.61347657 -1.24684819
## [313,] 1.79798385 -1.47297123
## [314,] -0.34147902 -5.86104463
## [315,] 2.09212264 -4.00090004
## [316,] 0.39607956 -0.95059141
## [317,] -0.65793159 0.94534120
## [318,] -1.34684266 -6.13638116
## [319,] 2.38808073 -1.67376514
## [320,] -2.86742751 0.99970948
## [321,] -0.20126403 -1.65526907
## [322,] -8.62693095 -0.18990246
## [323,] -3.51635414 -4.47943144
## [324,] -2.11070452 -6.18515375
## [325,] -5.27369071 -5.56910050
## [326,] 1.30916735 -0.04738228
## [327,] 0.50511386 -2.47109878
## [328,] 6.58819390 -6.28288908
## [329,] -2.26791748 -0.01763512
## [330,] -7.05123884 3.80685356
## [331,] -1.33519113 0.60702924
## [332,] -4.75940155 3.26501970
## [333,] 5.71452372 -1.76332171
## [334,] 0.36539667 -2.76537368
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## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
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## [319,] 0.904292322 -5.70144490
## [320,] 3.589982178 2.75878003
## [321,] 3.230941534 -1.60428335
## [322,] 4.040818145 4.21874752
## [323,] 5.315799998 1.05913155
## [324,] -4.473939690 6.09271218
## [325,] 4.158580056 4.57315240
## [326,] 9.562202925 -2.79447655
## [327,] 2.534959424 -3.56293602
## [328,] 5.782846838 5.15408721
## [329,] 3.067868410 5.33927040
## [330,] 3.593221389 5.27875750
## [331,] 0.550547352 3.68929964
## [332,] -1.416045262 9.90654355
## [333,] 3.597094935 4.95965605
## [334,] 3.516275901 4.53106034
## [335,] 2.012546746 4.26537668
## [336,] 0.880316146 0.52380214
## [337,] 6.604572954 8.20930028
## [338,] 1.254011723 -1.31865736
## [339,] 3.697931608 8.78309170
## [340,] 4.668291796 8.32522337
## [341,] 9.176817599 -2.59561107
## [342,] 5.269620010 3.13363518
## [343,] 7.982783583 -0.31360958
## [344,] 1.978848938 -5.60466983
## [345,] -2.046774736 2.92041282
## [346,] 8.822866011 4.23329054
## [347,] 0.309732971 0.38772055
## [348,] 1.459975746 11.54795707
## [349,] -0.450895408 3.09062783
## [350,] 0.900472987 3.93966430
## [351,] 10.891636378 3.64886044
## [352,] 1.817122675 1.75217001
## [353,] 6.815703403 -5.49291541
## [354,] 5.384883112 4.31964050
## [355,] -5.620710646 2.10377456
## [356,] 6.361914105 0.59812880
## [357,] 5.782041942 5.93602698
## [358,] -2.019207057 6.55075224
## [359,] 5.015447545 3.57576425
## [360,] 2.348621812 2.52679410
## [361,] -1.168384137 2.97218028
## [362,] -1.340419484 5.56686911
## [363,] -2.105019831 9.33248953
## [364,] 0.541868539 11.86126392
## [365,] 2.691762879 1.78840359
## [366,] -1.308565140 7.92242269
## [367,] 2.400219088 2.18207289
## [368,] 10.054828516 8.95306264
## [369,] 3.862133982 4.22664432
## [370,] 0.160048803 -3.01419989
## [371,] 7.979831473 2.39906442
## [372,] 11.467470710 8.06875792
## [373,] 4.898501692 4.90705750
## [374,] -1.709210446 -1.50512682
## [375,] 4.684029271 6.80403731
## [376,] 2.160915623 1.42846139
## [377,] 0.612064024 5.88923483
## [378,] 2.807343859 1.96540211
## [379,] -0.520908676 -0.30221348
## [380,] 2.529502631 1.16545771
## [381,] 3.456465629 2.45697070
## [382,] 2.659945182 3.04481723
## [383,] 4.968370231 3.28470581
## [384,] 2.207112265 -0.11558823
## [385,] 9.135056902 10.12712809
## [386,] 8.819006658 10.50750555
## [387,] 2.396661984 7.55413373
## [388,] 10.527508792 9.33470995
## [389,] 3.094446495 1.59126311
## [390,] -5.438396550 4.95758960
## [391,] -1.094205048 4.62595264
## [392,] 2.415136693 3.86116086
## [393,] 6.185329607 3.40483730
## [394,] 4.320529051 1.20559341
## [395,] 1.101693671 -0.31489595
## [396,] 2.597729089 6.91520856
## [397,] 2.012737004 1.85258380
## [398,] 11.735269989 7.58994804
## [399,] 1.370644764 2.04756270
## [400,] 4.905860582 3.84141139
## [401,] 2.431185229 8.95672484
## [402,] 4.054731818 8.13231111
## [403,] 5.732199979 9.52481359
## [404,] 11.510522970 7.17468504
## [405,] 5.640653612 2.36701971
## [406,] 2.348295604 0.44424460
## [407,] 3.969948666 5.52217250
## [408,] 10.040550721 9.52206266
## [409,] 0.127933500 4.92229621
## [410,] 0.275128414 1.01807517
## [411,] 7.497075764 -1.46621533
## [412,] 6.257279690 4.60212336
## [413,] 8.227624628 1.25656912
## [414,] 9.668344980 9.97727246
## [415,] -3.120033698 2.99527017
## [416,] 2.986645409 3.34343354
## [417,] 12.371613277 3.93844162
## [418,] 0.406843396 1.81819183
## [419,] 6.291574180 0.02509002
## [420,] -2.930343511 4.51359878
## [421,] 6.295934159 8.37963855
## [422,] 5.136035983 -2.05912689
## [423,] 2.186037209 2.68556107
## [424,] 5.984766742 0.90389069
## [425,] -5.766573831 4.00693406
## [426,] -3.496292935 -2.53427522
## [427,] -0.190670536 1.06390585
## [428,] 2.242120856 2.08655798
## [429,] 8.279302128 4.16102730
## [430,] -3.839525004 -2.02795527
## [431,] 0.003471395 5.44747087
## [432,] 10.953658760 8.01420126
## [433,] 6.290613556 9.27657381
## [434,] 5.269315561 0.37775696
## [435,] 11.440846174 6.56971258
## [436,] -5.408638473 1.46235132
## [437,] 3.151357431 11.98352873
## [438,] 0.682903711 8.72488425
## [439,] 4.591095074 0.45655874
## [440,] 3.825738467 3.49766456
## [441,] 0.154240854 -0.07493186
## [442,] 1.312446097 3.48100310
## [443,] 3.770957206 9.55647721
## [444,] 7.557470323 5.53488714
## [445,] -0.478016034 -1.55904877
## [446,] 4.293790862 2.79880873
## [447,] 1.159264512 4.04944852
## [448,] -2.506641088 8.92286181
## [449,] 2.432920092 1.98128074
## [450,] 1.926980883 2.35277355
## [451,] 1.484382524 1.33823042
## [452,] 5.354267314 6.92447337
## [453,] 3.430786993 3.42663045
## [454,] 1.030329927 11.74495008
## [455,] 0.481141069 4.50924221
## [456,] 12.023406375 5.56292852
## [457,] 12.140530482 4.82232819
## [458,] 6.103171029 2.93546361
## [459,] -0.530798550 4.95830831
## [460,] 2.146051349 11.82039942
## [461,] 8.714610733 6.75769737
## [462,] -4.221146638 -1.66955543
## [463,] -0.024702124 2.35388165
## [464,] -4.994585319 3.01690199
## [465,] -3.106406200 3.99122703
## [466,] 4.695113362 4.49034629
## [467,] 4.449164610 7.07743077
## [468,] 3.526841000 2.00257253
## [469,] 3.504708222 0.90708156
## [470,] 4.127056546 1.66404595
## [471,] 3.801838951 3.30767547
## [472,] 3.900502598 1.68826062
## [473,] 2.678279583 3.59124823
## [474,] 2.501957433 4.81788249
## [475,] 1.933416939 2.20056949
## [476,] 3.729512890 3.64246442
## [477,] 3.881395674 7.02355022
## [478,] 2.227362780 2.24243547
## [479,] 2.506321792 3.26072976
## [480,] 5.331595322 1.50021628
## [481,] -0.236070405 4.24079380
## [482,] 6.121365011 1.92680490
## [483,] 2.628527902 4.70808412
## [484,] 3.880501984 2.10360872
## [485,] 5.165120323 1.37472166
## [486,] -0.129562320 2.59776771
## [487,] -1.074610456 11.17639745
## [488,] 3.566587080 3.10862719
## [489,] 2.633124826 3.93986701
## [490,] 4.994039819 5.71289413
## [491,] 4.982189065 1.94208767
## [492,] 4.386278344 4.41618098
## [493,] 2.651710380 1.58970219
## [494,] 3.012851406 3.23738906
## [495,] 2.266831510 3.46629030
## [496,] 2.678579458 3.43777936
## [497,] 0.962589902 3.02036640
## [498,] 1.957168862 2.73130697
## [499,] 1.558713613 2.77179745
## [500,] 2.427941098 3.73994960
## [501,] 1.333212537 1.76037404GGM.all.alive.min = as.data.frame(GGMnetworkStats(P0.all.alive.min$sparseParCor, as.table = T))
GGM.all.dead.min = as.data.frame(GGMnetworkStats(P0.all.dead.min$sparseParCor, as.table = T))
GGM.all.alive.min.order = GGM.all.alive.min[order(GGM.all.alive.min$degree, decreasing = T), ]
GGM.all.dead.min.order = GGM.all.dead.min[order(GGM.all.dead.min$degree, decreasing = T), ]
#Output top 5%
GGM.all.alive.min.order[1:round(nrow(GGM.all.alive.min.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos
## G6PD.RPPA 168 9845.7516 0.001371742 0.9453448 88 80
## PDCD4.RPPA 164 7375.9317 0.001377410 1.0000000 82 82
## TFRC.RPPA 163 8708.7640 0.001394700 0.9947696 81 82
## IGFBP2.RPPA 156 7023.3638 0.001360544 0.9777160 78 78
## GATA3.RPPA 151 6648.2105 0.001319261 0.8748329 82 69
## GAPDH.RPPA 145 6958.8498 0.001319261 0.8802702 81 64
## FASN.RPPA 144 6722.5402 0.001291990 0.8293319 75 69
## MYH11.RPPA 135 3912.3948 0.001312336 0.8981209 68 67
## SQSTM1.R2Gn 127 2625.0369 0.001305483 0.9124490 69 58
## GAPDH.R2Gn 123 2680.9953 0.001261034 0.8271108 74 49
## EEF2.R2Gn 121 2671.5811 0.001283697 0.8668991 60 61
## ATM.RPPA 117 3268.5137 0.001302083 0.7922948 65 52
## HSPA1A.R2Gn 101 1444.3206 0.001233046 0.7088702 51 50
## SYP.R2Gn 96 1069.2180 0.001201923 0.7043772 49 47
## TGM2.R2Gn 94 1489.8359 0.001215067 0.6604615 48 46
## FN1.R2Gn 90 1434.0092 0.001172333 0.6484073 46 44
## TTF1.RPPA 86 1207.6249 0.001190476 0.6322846 52 34
## hsa-mir-206 81 910.2568 0.001164144 0.5812520 41 40
## hsa-mir-216b 81 654.0348 0.001206273 0.6334391 49 32
## MSH6.RPPA 81 1181.7849 0.001177856 0.6161348 46 35
## RPS6.R2Gn 80 918.8240 0.001199041 0.5985855 46 34
## SERPINE1.R2Gn 79 879.6468 0.001183432 0.5969885 38 41
## hsa-mir-383 78 1178.5310 0.001176471 0.5915320 39 39
## IGFBP2.R2Gn 78 822.4111 0.001183432 0.6074979 42 36
## hsa-mir-137 75 396.7228 0.001173709 0.6337584 38 37
## hsa-mir-122 74 775.8305 0.001141553 0.5259819 43 31
## PTEN.RPPA 74 952.6622 0.001199041 0.5805945 34 40
## hsa-mir-1251 72 626.9116 0.001180638 0.5992614 37 35
## hsa-mir-1305 70 899.4704 0.001157407 0.5188689 34 36
## hsa-mir-135a-2 69 405.9010 0.001204819 0.5766997 34 35
## hsa-mir-429 68 659.2839 0.001156069 0.4851670 33 35
## RBM15.RPPA 68 409.9047 0.001187648 0.5692950 35 33
## EGFR.RPPA 68 587.1310 0.001152074 0.5176734 38 30
## hsa-mir-873 67 568.0790 0.001177856 0.5188944 34 33
## hsa-mir-1258 66 727.7608 0.001153403 0.4568887 28 38
## mutualInfo variance partialVar
## G6PD.RPPA 0.20204728 1.223906 1
## PDCD4.RPPA 0.18646575 1.204983 1
## TFRC.RPPA 0.17529089 1.191593 1
## IGFBP2.RPPA 0.17936641 1.196459 1
## GATA3.RPPA 0.15863750 1.171913 1
## GAPDH.RPPA 0.20634456 1.229177 1
## FASN.RPPA 0.12961406 1.138389 1
## MYH11.RPPA 0.16381684 1.177999 1
## SQSTM1.R2Gn 0.10818455 1.114253 1
## GAPDH.R2Gn 0.14126705 1.151732 1
## EEF2.R2Gn 0.10511574 1.110839 1
## ATM.RPPA 0.12044601 1.128000 1
## HSPA1A.R2Gn 0.07793508 1.081052 1
## SYP.R2Gn 0.05796163 1.059674 1
## TGM2.R2Gn 0.07862171 1.081795 1
## FN1.R2Gn 0.05434665 1.055851 1
## TTF1.RPPA 0.06133206 1.063252 1
## hsa-mir-206 0.05072024 1.052029 1
## hsa-mir-216b 0.05203778 1.053416 1
## MSH6.RPPA 0.04204393 1.042940 1
## RPS6.R2Gn 0.04975728 1.051016 1
## SERPINE1.R2Gn 0.05419564 1.055691 1
## hsa-mir-383 0.05170538 1.053065 1
## IGFBP2.R2Gn 0.07318399 1.075928 1
## hsa-mir-137 0.04902682 1.050249 1
## hsa-mir-122 0.04732383 1.048461 1
## PTEN.RPPA 0.05635560 1.057974 1
## hsa-mir-1251 0.04476313 1.045780 1
## hsa-mir-1305 0.03970958 1.040509 1
## hsa-mir-135a-2 0.04117753 1.042037 1
## hsa-mir-429 0.04198681 1.042881 1
## RBM15.RPPA 0.04688632 1.048003 1
## EGFR.RPPA 0.04771507 1.048872 1
## hsa-mir-873 0.04364237 1.044609 1
## hsa-mir-1258 0.03230406 1.032832 1GGM.all.dead.min.order[1:round(nrow(GGM.all.dead.min.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos
## FN1.R2Gn 241 21986.3754 0.0012953368 1.0000000 122 119
## MYH11.RPPA 211 12701.5501 0.0012562814 0.9686400 109 102
## TFRC.RPPA 211 12453.4951 0.0012610340 0.9951659 113 98
## GATA3.RPPA 206 13737.5974 0.0012562814 0.9363932 106 100
## HSPA1A.R2Gn 192 9672.2517 0.0011668611 0.9061097 88 104
## GAPDH.RPPA 173 8678.2950 0.0011933174 0.8415024 90 83
## GAPDH.R2Gn 166 5633.4569 0.0011876485 0.8468065 83 83
## EEF2.R2Gn 155 4693.5947 0.0011820331 0.8341235 75 80
## SYP.R2Gn 140 3063.8040 0.0011467890 0.7893836 65 75
## FASN.RPPA 139 2868.0309 0.0011415525 0.7532201 67 72
## RPS6.R2Gn 137 3012.6079 0.0011325028 0.7464105 80 57
## SQSTM1.R2Gn 136 3326.3740 0.0011520737 0.7578852 74 62
## ATM.RPPA 128 3596.3706 0.0010905125 0.7191972 66 62
## PDCD4.RPPA 125 2441.9224 0.0011415525 0.7494179 68 57
## CTNNB1.R2Gn 122 3012.5137 0.0011223345 0.6899288 57 65
## G6PD.RPPA 120 3714.3493 0.0011185682 0.6842527 63 57
## hsa-mir-122 111 1452.1374 0.0010834236 0.6695921 59 52
## hsa-mir-499 109 2079.9301 0.0010482180 0.5844634 58 51
## hsa-mir-124-3 95 813.6369 0.0010893246 0.6365824 53 42
## ADAR.R2Gn 93 1423.4171 0.0010582011 0.5500443 53 40
## CCND1.R2Gn 91 1067.6224 0.0010928962 0.5488467 47 44
## TUBA1B.R2Gn 88 900.5169 0.0010537408 0.5687985 40 48
## MSH6.RPPA 87 793.7687 0.0010649627 0.5188161 41 46
## hsa-mir-135a-2 83 419.6551 0.0010718114 0.6034760 39 44
## hsa-mir-34b 83 510.9229 0.0010718114 0.5668682 44 39
## hsa-mir-3662 83 448.5196 0.0010741139 0.5837708 44 39
## hsa-mir-137 81 873.6690 0.0010764263 0.5300668 43 38
## hsa-mir-1197 77 2031.1748 0.0010235415 0.3370275 20 57
## hsa-mir-200a 76 486.1722 0.0010010010 0.4742470 33 43
## hsa-mir-216a 75 538.8298 0.0010471204 0.4880645 37 38
## PTEN.RPPA 75 442.6918 0.0010288066 0.4798766 43 32
## hsa-mir-656 74 1495.5721 0.0009930487 0.3396354 18 56
## hsa-mir-206 72 375.6631 0.0010090817 0.4888304 31 41
## hsa-mir-124-2 71 318.6859 0.0010172940 0.4987746 39 32
## hsa-mir-329-1 70 1363.3646 0.0009643202 0.2790516 20 50
## mutualInfo variance partialVar
## FN1.R2Gn 0.20482810 1.227314 1
## MYH11.RPPA 0.15896348 1.172295 1
## TFRC.RPPA 0.16071776 1.174353 1
## GATA3.RPPA 0.14906958 1.160754 1
## HSPA1A.R2Gn 0.14647187 1.157742 1
## GAPDH.RPPA 0.12226536 1.130054 1
## GAPDH.R2Gn 0.10105121 1.106333 1
## EEF2.R2Gn 0.07879298 1.081980 1
## SYP.R2Gn 0.06862457 1.071034 1
## FASN.RPPA 0.06840337 1.070797 1
## RPS6.R2Gn 0.06674720 1.069025 1
## SQSTM1.R2Gn 0.06562988 1.067831 1
## ATM.RPPA 0.05239122 1.053788 1
## PDCD4.RPPA 0.06294247 1.064966 1
## CTNNB1.R2Gn 0.05641572 1.058037 1
## G6PD.RPPA 0.04567924 1.046739 1
## hsa-mir-122 0.03210511 1.032626 1
## hsa-mir-499 0.04446979 1.045473 1
## hsa-mir-124-3 0.03124435 1.031738 1
## ADAR.R2Gn 0.03969341 1.040492 1
## CCND1.R2Gn 0.04207702 1.042975 1
## TUBA1B.R2Gn 0.02590811 1.026247 1
## MSH6.RPPA 0.04582815 1.046894 1
## hsa-mir-135a-2 0.03239691 1.032927 1
## hsa-mir-34b 0.03310570 1.033660 1
## hsa-mir-3662 0.02983565 1.030285 1
## hsa-mir-137 0.02385379 1.024141 1
## hsa-mir-1197 0.01909698 1.019280 1
## hsa-mir-200a 0.02661153 1.026969 1
## hsa-mir-216a 0.02448512 1.024787 1
## PTEN.RPPA 0.02010053 1.020304 1
## hsa-mir-656 0.01957779 1.019771 1
## hsa-mir-206 0.02767371 1.028060 1
## hsa-mir-124-2 0.02568195 1.026015 1
## hsa-mir-329-1 0.01695770 1.017102 1Colors.min.plot.a <- rownames(GGM.all.alive.min.order)
Colors.min.plot.a[grep("hsa", rownames(GGM.all.alive.min.order))] <- "miRNASeqGene"
Colors.min.plot.a[grep(".RPPA", rownames(GGM.all.alive.min.order))] <- "RPPA Array"
Colors.min.plot.a[grep(".R2Gn", rownames(GGM.all.alive.min.order))] <- "RNASeq2GeneNorm"ggplot(GGM.all.alive.min.order, aes(x = reorder(rownames(GGM.all.alive.min.order), -degree), y = degree, color = Colors.min.plot.a)) +
geom_point() +
geom_hline(yintercept = mean(GGM.all.alive.min.order$degree), linetype = "dashed", color = "red") +
# 36th unit: top 5%
geom_hline(yintercept = GGM.all.alive.min.order[36,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "All Data", guide = guide_axis(angle = 90)) +
scale_color_manual(values = c("red", "blue", "green3"))+
ggtitle("Variables sorted by degree, FDR = 1-1e-14, Surviving Patients")
Colors.min.plot.d <- rownames(GGM.all.dead.min.order)
Colors.min.plot.d[grep("hsa", rownames(GGM.all.dead.min.order))] <- "miRNASeqGene"
Colors.min.plot.d[grep(".RPPA", rownames(GGM.all.dead.min.order))] <- "RPPA Array"
Colors.min.plot.d[grep(".R2Gn", rownames(GGM.all.dead.min.order))] <- "RNASeq2GeneNorm"ggplot(GGM.all.dead.min.order, aes(x = reorder(rownames(GGM.all.dead.min.order), -degree), y = degree, color = Colors.min.plot.d)) +
geom_point() +
geom_hline(yintercept = mean(GGM.all.dead.min.order$degree), linetype = "dashed", color = "red") +
# 36th unit: top 5%
geom_hline(yintercept = GGM.all.dead.min.order[36,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "All Data", guide = guide_axis(angle = 90)) +
scale_color_manual(values = c("red", "blue", "green3"))+
ggtitle("Variables sorted by degree, FDR = 1-1e-14, Deceased Patients")
FDRcut 1-1e-6:
P0.all.alive.6 = sparsify(opt.all.alive$optPrec, threshold = "localFDR", FDRcut=1-1e-6)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 11475
## - Corresponding to 4.68 % of possible edges
## P0.all.dead.6 = sparsify(opt.all.dead$optPrec, threshold = "localFDR", FDRcut=1-1e-6)## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting
##
## - Retained elements: 12207
## - Corresponding to 4.98 % of possible edges
## PcorP.a.6 = pruneMatrix(P0.all.alive.6$sparseParCor)
Colors.a.6 <- rownames(PcorP.a.6)
Colors.a.6[grep("hsa", rownames(PcorP.a.6))] <- "red"
Colors.a.6[grep(".RPPA", rownames(PcorP.a.6))] <- "green"
Colors.a.6[grep(".R2Gn", rownames(PcorP.a.6))] <- "cyan"PcorP.d.6 = pruneMatrix(P0.all.dead.6$sparseParCor)
Colors.d.6 <- rownames(PcorP.d.6)
Colors.d.6[grep("hsa", rownames(PcorP.d.6))] <- "red"
Colors.d.6[grep(".RPPA", rownames(PcorP.d.6))] <- "green"
Colors.d.6[grep(".R2Gn", rownames(PcorP.d.6))] <- "cyan"set.seed(42)
Ugraph(PcorP.a.6, type = "fancy", lay = "layout_with_fr", Vsize = 2, Vcex = .3, prune = T, cut = 0.5,
Vcolor = Colors.a.6,
main = "All Numerical data (Surviving patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\nRed: miRNASeqGene; Blue: RNASeq2GeneNorm; Green: RPPA Array")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## [,1] [,2]
## [1,] -4.30701986 -3.742865820
## [2,] -5.45488551 -0.929373442
## [3,] 0.21956118 1.016141793
## [4,] 8.17462438 -5.686197543
## [5,] -0.98981579 -10.714395830
## [6,] 0.09135922 -1.831589281
## [7,] 0.72862731 -1.609869599
## [8,] -5.26430024 1.295265842
## [9,] 8.45781515 -2.589686377
## [10,] -5.49392788 -5.163060979
## [11,] -0.82581588 -1.624163064
## [12,] 2.09544760 -1.123058660
## [13,] 1.68872994 -0.956949181
## [14,] 1.71236375 -1.160444408
## [15,] 1.44079185 -1.041208938
## [16,] -1.14828922 -1.016236574
## [17,] -0.40661424 0.555186883
## [18,] -0.11010096 -6.827509097
## [19,] -0.51171889 -0.972942746
## [20,] 1.03213654 3.097717050
## [21,] 0.62080581 -1.208090794
## [22,] -0.41205562 -2.196580119
## [23,] 0.34062271 -2.791589425
## [24,] -0.92427913 -1.554878825
## [25,] -0.36775346 -0.776370655
## [26,] -1.15632820 -4.061059443
## [27,] -0.97065128 0.279545973
## [28,] 0.29883787 -1.654061858
## [29,] 1.69110892 -2.224453058
## [30,] -0.92524829 -0.432603981
## [31,] -0.48721594 -3.152805140
## [32,] -3.53330010 -1.475145261
## [33,] -2.55657753 -3.732936441
## [34,] -0.03960073 -2.944089140
## [35,] -1.32180494 -5.124458877
## [36,] -1.05033392 -1.389517744
## [37,] 3.62804607 0.212395902
## [38,] -0.15113867 -2.272225843
## [39,] 0.57890268 -1.027566424
## [40,] -0.65802981 0.398272342
## [41,] -2.13151509 -1.932582501
## [42,] -5.61820484 3.671229302
## [43,] -5.09666905 4.018986562
## [44,] -3.41719262 0.102571243
## [45,] 0.14566207 -1.114695437
## [46,] 0.28028731 -2.137551636
## [47,] -0.25032360 3.123318054
## [48,] 0.28066219 -2.087291491
## [49,] -1.95443624 -2.180663595
## [50,] 4.04546589 2.722665280
## [51,] -5.66181906 -8.402399337
## [52,] -0.49748388 -0.670898381
## [53,] -1.06154584 -1.860130644
## [54,] 3.88831445 2.522947335
## [55,] -8.88591149 3.168537301
## [56,] -4.29119545 1.747312977
## [57,] 0.38529391 -1.218603703
## [58,] 3.96062630 -1.622934796
## [59,] -0.39606179 -0.067672784
## [60,] -0.28115381 -2.040262524
## [61,] -0.29548609 -2.768153986
## [62,] 3.26910970 -0.373302231
## [63,] -0.77493485 -1.256442929
## [64,] -0.63329795 -2.555137174
## [65,] 0.22137136 -0.816815897
## [66,] 1.78974895 -2.765335794
## [67,] -2.47659755 -0.579621613
## [68,] 8.37689314 -0.523715600
## [69,] -7.22548161 -7.646980920
## [70,] -3.27710171 -3.418737284
## [71,] 1.67675052 2.256851117
## [72,] 1.97126620 3.228781334
## [73,] -4.05626329 0.371650436
## [74,] 2.16963523 -3.090962208
## [75,] 0.01464990 -1.816125547
## [76,] -0.22648397 -0.680362611
## [77,] 2.99565495 -0.013104907
## [78,] 4.31530751 -3.296256041
## [79,] 2.50730821 -7.950600234
## [80,] -2.12673294 6.331936289
## [81,] -5.27675080 -6.620493711
## [82,] 8.56370870 -2.101774600
## [83,] -6.75265622 -7.368966780
## [84,] 2.80944071 2.240127775
## [85,] 2.35426641 -4.135211076
## [86,] -2.15055002 -2.638851834
## [87,] -2.22795840 -2.442558121
## [88,] 0.11218789 -2.628540317
## [89,] -9.13054655 0.365478219
## [90,] 0.09874020 -1.928476064
## [91,] 7.97189821 -4.085372338
## [92,] 4.11201486 -6.638364201
## [93,] -3.19770708 4.249397202
## [94,] -0.05840919 -0.937891324
## [95,] -0.93432150 -2.359823134
## [96,] -3.20745171 0.475471236
## [97,] 5.81381155 -8.602111184
## [98,] -4.22798707 -0.390806606
## [99,] -4.36747631 0.113815718
## [100,] -2.05908173 -4.069590918
## [101,] -0.49144989 -4.018254903
## [102,] -0.85535514 -2.610822047
## [103,] -1.29564679 -3.511335443
## [104,] -5.81829892 -2.167231691
## [105,] -5.97624863 0.868487013
## [106,] -4.95817041 1.895048443
## [107,] -6.49785977 1.178505166
## [108,] -1.67074613 -1.243424889
## [109,] -1.44637353 -0.581280832
## [110,] -4.81077101 -1.819893192
## [111,] -1.31477862 -4.288406271
## [112,] -1.84429984 -1.064402957
## [113,] 0.57371944 -1.357189472
## [114,] 0.18558792 -1.782372187
## [115,] -0.55756909 -7.991547204
## [116,] 7.23432786 -4.963876585
## [117,] -2.60891187 -3.165697999
## [118,] -0.77869989 -2.711575373
## [119,] -3.96140924 2.053680530
## [120,] -3.61321750 -1.858258496
## [121,] -1.11901011 -1.329271757
## [122,] -1.52012483 -1.173587557
## [123,] -0.32508375 0.203015004
## [124,] 0.09389619 -2.307012719
## [125,] 1.51034315 0.391991858
## [126,] 8.36845809 -3.476478549
## [127,] -2.86682514 5.155096126
## [128,] -2.14059395 4.101842844
## [129,] 8.27611843 0.029091600
## [130,] -2.50364777 -2.931033470
## [131,] -9.72142632 -1.335642575
## [132,] 8.37781618 -3.059381762
## [133,] -2.37218970 7.835796270
## [134,] -6.38047828 0.389917360
## [135,] -9.12720120 -4.732590306
## [136,] 1.06040125 -3.217489703
## [137,] -9.70304526 -2.020325162
## [138,] -6.86135992 2.126705507
## [139,] -1.60904084 -1.941546506
## [140,] 3.95582759 -0.573934901
## [141,] 6.99706030 -7.542521678
## [142,] -8.52922561 3.589166178
## [143,] 4.63211903 -6.137363326
## [144,] -0.57765076 -3.096003494
## [145,] -3.01459581 -2.593416123
## [146,] -5.47790317 -7.791551947
## [147,] 2.38836386 -2.710324933
## [148,] -9.59852550 -3.805515143
## [149,] -0.56258348 -0.756234392
## [150,] 7.73091742 -5.039057978
## [151,] -2.11765294 -1.265572000
## [152,] -0.10820500 -1.687197897
## [153,] 0.06669448 -3.051593602
## [154,] -2.24270644 -1.048732717
## [155,] 0.13221739 -0.944199950
## [156,] -0.44969902 -1.986725055
## [157,] -0.95524185 -2.896559567
## [158,] -0.59050817 -0.032897985
## [159,] -0.23082323 -1.195473708
## [160,] 0.45046469 -2.015432529
## [161,] -0.02577953 -2.109204116
## [162,] 1.93736801 -3.562076303
## [163,] 8.09890055 -4.700875363
## [164,] -0.21265373 -0.367932534
## [165,] -0.45472058 -1.165489965
## [166,] 3.42025377 -0.689281860
## [167,] 3.62543497 -0.694860180
## [168,] -0.57211364 1.798897755
## [169,] 3.30457653 1.921446324
## [170,] 1.57237776 -1.127825347
## [171,] 1.44600134 -0.521985748
## [172,] -3.10762982 3.657648887
## [173,] 7.37078590 1.480438960
## [174,] -4.03443475 -2.280974945
## [175,] 6.27519003 -1.605473846
## [176,] 2.88002489 -3.855109440
## [177,] 6.61732881 -7.676357888
## [178,] -0.05424570 2.264793852
## [179,] -5.14907369 0.254240183
## [180,] -1.18559856 -0.800051677
## [181,] 2.56817794 1.623035935
## [182,] -0.91124947 -0.745894777
## [183,] -0.39359880 -3.791971730
## [184,] -7.00710086 5.468238178
## [185,] 2.94342959 -7.591975249
## [186,] -1.38825779 -1.558087440
## [187,] -8.83994146 -5.289992824
## [188,] 0.40229478 -1.495599424
## [189,] -6.22239222 -7.999661002
## [190,] -0.67557646 -1.763757833
## [191,] 0.81381360 2.603791830
## [192,] -1.06122903 -2.401632011
## [193,] 2.78027659 3.582011700
## [194,] -3.88848656 -7.603864600
## [195,] -1.69033497 0.046209556
## [196,] 7.60623764 -6.270526835
## [197,] 6.29722070 -8.164908010
## [198,] 0.63730082 -0.211352759
## [199,] -0.74843692 -1.372471640
## [200,] -1.98798597 -10.613789433
## [201,] 0.55690069 -2.214657819
## [202,] -1.13437709 -2.035247852
## [203,] -0.78221836 -2.912053986
## [204,] -7.62799729 -0.627300811
## [205,] -0.92678506 -0.927951368
## [206,] 0.43459643 -1.615359534
## [207,] -0.45196262 -1.628378275
## [208,] 4.19337740 -1.336762014
## [209,] -1.08278448 -0.690478920
## [210,] 3.82738901 -0.980998829
## [211,] -0.29206930 -1.367878346
## [212,] -7.35107389 5.033677545
## [213,] -3.40892562 -2.879482982
## [214,] 1.94897018 -0.656465383
## [215,] 1.89843531 -0.982373434
## [216,] 3.67538265 -1.395308820
## [217,] 4.24034066 -0.734277337
## [218,] 1.85243574 0.412690771
## [219,] -1.49267583 -10.710941611
## [220,] 4.13534152 -1.048256281
## [221,] 1.66507039 -0.748959208
## [222,] 3.72690063 -1.186383573
## [223,] 3.43025669 -0.236025640
## [224,] -0.86467087 -1.876102313
## [225,] -0.90201785 -3.947041613
## [226,] 4.78375557 -2.288747969
## [227,] 0.17842148 -1.531988440
## [228,] -1.83048114 0.379850134
## [229,] -1.29238167 -1.783534271
## [230,] -2.19347963 0.027555747
## [231,] -1.72586549 -2.251349758
## [232,] -1.33585893 -3.265443863
## [233,] -0.10842585 -1.272522484
## [234,] 0.21836652 -1.085887785
## [235,] 3.56432318 -0.404487473
## [236,] 3.79962455 -0.199536160
## [237,] 2.29200603 -1.188012984
## [238,] 2.51730826 -1.985304199
## [239,] 7.15008503 -6.799533781
## [240,] -6.07322609 -0.958881288
## [241,] -0.74287575 -1.954754212
## [242,] 3.64641057 -0.907558992
## [243,] 4.19506849 0.244462134
## [244,] -0.03159250 -0.314825932
## [245,] 2.78075333 -0.446309984
## [246,] -5.00416534 -1.529478865
## [247,] -5.24436196 -3.944953505
## [248,] -5.38115013 -3.479532557
## [249,] -1.87104290 -4.506167652
## [250,] -3.35589909 -2.226727928
## [251,] 1.51563932 1.163040677
## [252,] -1.47196248 -1.606249545
## [253,] -0.84285607 -3.593397429
## [254,] 2.98206348 -0.831169141
## [255,] -3.00035778 -3.761977461
## [256,] 2.42656792 -0.153428576
## [257,] 3.49305788 -0.018267575
## [258,] -0.21716593 -1.690954200
## [259,] -1.28837409 -1.473892183
## [260,] -0.64680947 -1.232870033
## [261,] -6.99224228 -3.421033802
## [262,] 3.58451758 -1.879719780
## [263,] 2.19399882 0.045794321
## [264,] 3.92048893 0.010026343
## [265,] 2.89424693 0.361636867
## [266,] -3.39022417 -3.953198926
## [267,] -0.29706482 -0.985858734
## [268,] -0.83570658 -5.603233049
## [269,] -0.81354624 -1.063143044
## [270,] 1.16226904 -4.775909346
## [271,] -0.03662345 -4.144519918
## [272,] 0.41540166 -5.005807015
## [273,] 0.88272024 -5.257997896
## [274,] 0.62943372 -5.341106847
## [275,] 0.43146225 -5.576551586
## [276,] -0.19136366 -4.173592563
## [277,] 1.34144085 -4.229853841
## [278,] 0.97111955 -2.820936217
## [279,] 0.14558817 -4.068455980
## [280,] 0.27299142 -4.201716456
## [281,] 1.17845250 -5.120725263
## [282,] -0.24589603 -5.542555261
## [283,] 0.91669051 -4.791732807
## [284,] 0.64037358 -4.860554361
## [285,] 0.78752124 -4.622661489
## [286,] 0.10695993 -3.781027789
## [287,] -0.18386190 -2.974231250
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## [290,] -0.13307888 -1.470702174
## [291,] -0.14246105 -0.831785832
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## [296,] 0.69552686 -1.963622521
## [297,] 0.89416082 -1.244756804
## [298,] -0.52997790 -2.331106213
## [299,] 3.12899119 -0.157912985
## [300,] 1.74772236 -0.466358523
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## [302,] 2.55524365 -0.661904142
## [303,] 1.12938690 -0.723240399
## [304,] -2.89083772 -4.845690944
## [305,] -0.34126183 -2.071710979
## [306,] -0.84958297 -0.535328712
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## [326,] -3.16655584 -10.265938823
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## [341,] 1.22889767 -1.115717529
## [342,] -6.53419275 5.865637080
## [343,] -0.64979003 -1.490969663
## [344,] 1.38071843 -0.813436483
## [345,] 2.15516424 -1.034358673
## [346,] -1.04163670 -1.244246184
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## [351,] -1.26218631 -1.076276330
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## [360,] -1.31991540 -2.282567461
## [361,] -1.65313983 -2.577296359
## [362,] -6.01144715 -1.372650416
## [363,] 3.18510400 0.260079434
## [364,] -0.05157095 -1.209304929
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## [366,] 0.09873395 -0.397228369
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## [377,] -0.22707012 3.586841206
## [378,] -9.68328859 -3.197356677
## [379,] -6.41751965 -4.305238402
## [380,] -0.96776081 -0.611986399
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## [382,] -2.39432717 -1.643453975
## [383,] -1.23310360 -6.025730652
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## [385,] -0.47621534 -2.093927802
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## [387,] -1.12917032 -0.465941658
## [388,] -4.44956509 -1.296379000
## [389,] 2.07870769 -2.309096195
## [390,] -1.70126862 -3.576142398
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## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
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## [234,] -8.14670207 -5.562015284
## [235,] -5.48128977 -1.185591306
## [236,] -5.38720862 -10.739278934
## [237,] -8.53798078 -1.050095688
## [238,] -7.89470064 -4.763160385
## [239,] -7.30644308 -4.378358665
## [240,] -7.98570135 -3.999520269
## [241,] -8.46393906 -4.011708624
## [242,] -8.32489770 -5.425403714
## [243,] -5.26302514 5.050187421
## [244,] -9.05068916 -2.707559155
## [245,] -7.65813537 -5.603121580
## [246,] -7.60656493 -3.771629616
## [247,] -7.47079482 -5.124394627
## [248,] -7.91321697 -5.601308782
## [249,] -5.36256915 -1.101163999
## [250,] -5.39578609 -1.546483660
## [251,] -8.81052510 -2.407667697
## [252,] -3.25610793 -5.039081227
## [253,] -9.10539292 -1.991083367
## [254,] -5.82684258 -2.066104862
## [255,] -6.42646035 -1.290317553
## [256,] -5.31575518 -1.342427628
## [257,] -5.75726251 -2.229768187
## [258,] -3.43561028 -1.442806282
## [259,] -5.28720069 -2.391933648
## [260,] -4.50061855 -1.053644307
## [261,] -8.41895442 -4.687638217
## [262,] -7.61992802 -4.902380193
## [263,] -8.47825557 -4.971795269
## [264,] -6.48838366 -3.872756200
## [265,] -2.00901120 -4.267660788
## [266,] -5.12797965 -2.310880169
## [267,] -6.95998251 -4.455474962
## [268,] -7.66822642 -4.639358081
## [269,] -4.64115199 -5.361677771
## [270,] -7.03127662 -3.632607204
## [271,] 4.11870422 -2.423074572
## [272,] -7.82272967 -2.777933673
## [273,] -6.45557279 -3.259888323
## [274,] -8.69809331 3.131105417
## [275,] -4.48767789 -4.166095338
## [276,] -6.68870585 -3.168364767
## [277,] -3.87195403 -1.460268705
## [278,] -4.03930060 7.134155887
## [279,] -8.36259391 -4.971339809
## [280,] -5.40679003 -4.381783408
## [281,] -7.12905190 -3.496912055
## [282,] -8.23112538 -5.145276759
## [283,] -6.35346705 -0.820149196
## [284,] -4.86720307 -2.267943152
## [285,] -5.21719930 -1.732565079
## [286,] 3.94730270 -4.565645665
## [287,] -8.16117868 -3.201764243
## [288,] -7.81628525 -5.137427059
## [289,] -6.53397065 -4.433915679
## [290,] -8.00670557 -4.508648727
## [291,] -5.53289938 -0.659579817
## [292,] -1.60661404 6.054645005
## [293,] -3.20403621 6.906131797
## [294,] -4.71917556 7.169578408
## [295,] -14.42093756 -1.531240218
## [296,] -6.55098095 2.243693264
## [297,] -5.92842125 -0.885942899
## [298,] -10.32757115 6.415178627
## [299,] -5.65205191 -0.197412978
## [300,] -5.12134031 -0.643875290
## [301,] -5.21834080 0.061920598
## [302,] -5.04280023 0.007509082
## [303,] -4.82380219 -0.009800956
## [304,] -5.55398345 0.177100352
## [305,] -5.21491103 -1.118187919
## [306,] -4.36820912 -1.634660133
## [307,] -4.49908852 -1.999577228
## [308,] -5.48489500 0.736186219
## [309,] -6.07364271 -1.101965396
## [310,] -5.39725946 -0.170008952
## [311,] -4.97217631 -0.185508495
## [312,] -5.12492651 0.399119952
## [313,] -4.93492476 0.387620530
## [314,] -4.60063163 0.166234660
## [315,] -5.94816320 0.059692194
## [316,] -5.17581808 -1.994766029
## [317,] -6.34475086 -2.785900526
## [318,] -7.03986725 -2.426168490
## [319,] -6.80712703 -2.926698677
## [320,] -6.32116465 -2.663050376
## [321,] -5.50140036 -2.935014971
## [322,] -6.33252389 -3.292617368
## [323,] -7.00407717 -2.721695560
## [324,] -6.53406467 -3.052140493
## [325,] -6.06459297 -1.752588933
## [326,] -4.96808619 -2.598621975
## [327,] -6.72242393 -2.675743067
## [328,] -8.04590511 -5.259554282
## [329,] -7.17715449 -3.319146337
## [330,] -8.18014669 -4.302364031
## [331,] -4.85130256 -1.944471954
## [332,] -5.34093324 -1.825870209
## [333,] -5.81793117 -1.496954654
## [334,] -5.70756985 -1.334106555
## [335,] -4.61346602 -2.321163416
## [336,] -12.46079243 0.818054275
## [337,] -8.98615623 4.170422352
## [338,] -3.62304460 -1.260749736
## [339,] -4.29280020 -2.430924292
## [340,] -3.72026337 -1.069328161
## [341,] 3.04983092 1.450267568
## [342,] -6.40060576 0.386867492
## [343,] -2.93291893 4.688898282
## [344,] -5.84862193 -2.383482703
## [345,] -5.74055161 -3.018516079
## [346,] -3.61245686 -1.757527234
## [347,] -4.21201812 -0.578108059
## [348,] -7.32905318 -1.194834847
## [349,] -1.92802540 -5.138952454
## [350,] -6.92588448 -1.591216819
## [351,] -9.78520571 6.724092602
## [352,] -5.95193614 -3.173527143
## [353,] -1.34708228 0.112083645
## [354,] -5.66695913 -2.451555772
## [355,] -5.92782741 -0.225796314
## [356,] -5.91576417 0.866644931
## [357,] -4.13156641 4.658322082
## [358,] -5.43439712 -1.344937791
## [359,] -3.22786219 -1.744270907
## [360,] -9.87422351 2.613656114
## [361,] -14.28093656 -0.137156139
## [362,] -11.66768800 0.675552518
## [363,] 0.81019512 -2.599038856
## [364,] -6.94797019 0.826712196
## [365,] -4.71502622 -1.074057858
## [366,] -4.38155305 2.435970106
## [367,] -4.24144373 -8.632179270
## [368,] 3.43826490 1.553739039
## [369,] -10.54219401 2.359772267
## [370,] -5.74271657 -4.626403199
## [371,] -7.03227960 -5.339319555
## [372,] -8.66177342 -4.961034235
## [373,] -6.86444629 -3.411463434
## [374,] -7.56971495 -1.088256909
## [375,] -14.46921247 -3.365041587
## [376,] -6.64802503 -2.275497284
## [377,] 2.89182919 2.030365997
## [378,] -7.41669826 -2.777414356
## [379,] -6.94304138 -3.858136748
## [380,] -6.48212923 -0.742279236
## [381,] -6.07169898 -2.958669242
## [382,] -14.67378626 -0.650905875
## [383,] -6.20889201 -2.549960868
## [384,] -4.39893770 -1.818571639
## [385,] -3.40607926 -2.220516154
## [386,] -0.26733034 -2.870454767
## [387,] -8.69246013 -4.667290178
## [388,] -8.02084533 -1.279339390
## [389,] -6.64740352 -0.186804993
## [390,] -8.01140331 4.056726980
## [391,] -6.33193143 -2.294911693
## [392,] -7.18992943 -4.032426265
## [393,] -4.30789679 -2.138350221
## [394,] 3.01425646 -5.766159053
## [395,] -4.41136665 -2.671860340
## [396,] -7.71385378 4.427917856
## [397,] -4.56357695 -2.863741243
## [398,] -8.43697199 0.586624679
## [399,] -8.25158723 -4.584085432
## [400,] -5.64201404 -4.095933035
## [401,] -4.16674637 -0.911203761
## [402,] -4.12212419 -1.068259888
## [403,] -4.50874301 -0.733907410
## [404,] -1.40980784 6.298851213
## [405,] -5.73376657 -5.882945978
## [406,] -10.45702827 -0.085489231
## [407,] -5.04086807 -3.330398542
## [408,] -4.98450327 -1.701558894
## [409,] -4.79359223 -1.714450494
## [410,] -7.37748325 0.068397934
## [411,] -11.17660599 -0.587626847
## [412,] -1.37342705 1.307267860
## [413,] -9.18716449 -0.440146721
## [414,] -4.53791808 -7.159309336
## [415,] -3.13261092 0.781535196
## [416,] -4.78032784 3.229679716
## [417,] -6.38285799 0.138041588
## [418,] -8.82916848 -1.692581964
## [419,] -7.63748754 1.736518961
## [420,] -6.49011857 1.962896954
## [421,] -1.19960394 3.586347680
## [422,] -6.96000522 0.088731061
## [423,] -0.96230146 -2.172681900
## [424,] -4.10829128 0.855079715
## [425,] -1.06634421 -8.441843672
## [426,] -5.19638419 -3.870336420
## [427,] -2.56143814 -2.438091031
## [428,] -7.25949931 -1.541212117
## [429,] -7.79668603 0.599719075
## [430,] -3.57477801 -2.023108226
## [431,] -8.61017013 -10.158063493
## [432,] -11.09717161 1.159476624
## [433,] -6.17831198 -4.208372309
## [434,] -3.11200891 -0.813956485
## [435,] -8.53507291 0.144749784
## [436,] -3.96566163 -2.192087450
## [437,] -5.87344932 -1.827571755
## [438,] -3.63706852 -10.314840861
## [439,] -10.78319253 -3.095423663
## [440,] -4.52949191 -4.791018421
## [441,] -5.92126945 1.526275565
## [442,] -12.42193132 -3.280955739
## [443,] -11.30790525 -1.754170992
## [444,] -3.43248439 3.455996298
## [445,] -5.94405062 -2.548031637
## [446,] -5.52945079 1.474397169
## [447,] -6.22438443 -1.113742483
## [448,] -0.15741877 -6.263730274
## [449,] -4.93584228 -2.806608510
## [450,] 3.89993790 -2.036688180
## [451,] 2.69357553 -6.639378754
## [452,] -6.17358424 -4.759675639
## [453,] 3.35293482 0.820684950
## [454,] -4.37916842 1.328027024
## [455,] 3.82498803 -2.955106133
## [456,] -14.50134021 -2.845161529
## [457,] -4.11985942 -3.506578796
## [458,] -2.88890204 -4.792178944
## [459,] -2.22002137 -0.405378424
## [460,] -6.75399955 -1.830665272
## [461,] -2.99605640 -1.188012300
## [462,] -5.64269116 -1.157699905
## [463,] -4.21399868 -5.109406352
## [464,] -2.27672413 -2.216488337
## [465,] -5.32751239 -2.721672700
## [466,] -6.15632538 -2.055398613
## [467,] -0.50771489 -4.147351314
## [468,] -6.35964354 -1.544584021
## [469,] -4.20308331 -2.969979930
## [470,] -13.33915859 2.761641681
## [471,] -8.05660754 -0.552721977
## [472,] -1.45747114 2.301023933
## [473,] 0.30059619 -5.863064708
## [474,] -3.24381342 -0.464416191
## [475,] -5.59593711 7.139725874
## [476,] -3.40719032 -7.849218643
## [477,] -6.74718119 -1.726136343
## [478,] -2.99205528 -10.043035082
## [479,] -3.69285057 -1.507921049
## [480,] -5.35817421 -2.588609491
## [481,] -1.70948713 2.660869823
## [482,] -8.42606635 -2.288139364
## [483,] -6.06843116 -0.702781449
## [484,] -7.50764023 -0.322174936
## [485,] -7.23846818 -0.431728234
## [486,] -6.20100987 -0.766209114
## [487,] 0.43565397 -2.334150777
## [488,] -6.93127063 -0.841707597
## [489,] -7.11761042 -0.880938825
## [490,] -10.94724272 -8.800344697
## [491,] -5.34205083 -5.354537894
## [492,] -3.09961588 -3.068902148
## [493,] -2.63084108 -0.036495554
## [494,] -0.15683620 -4.729768044
## [495,] 1.22414591 -0.263231957
## [496,] -5.96456808 -10.693997041
## [497,] -4.65966567 -3.431666925
## [498,] -7.42217837 -2.125535407
## [499,] 3.38316175 -4.999403687
## [500,] -1.72168423 -5.940917629
## [501,] -3.65812483 -1.997954712
## [502,] -2.14720478 -6.276310416
## [503,] -3.89242403 -3.812278962
## [504,] -8.36442263 -0.682463447
## [505,] -5.11507020 -6.262680136
## [506,] 3.89757616 -1.408697866
## [507,] -11.97340140 4.733260500
## [508,] -12.02588691 -7.715852943
## [509,] -3.06863982 -5.458936945
## [510,] -3.89698996 -3.268025258
## [511,] 4.11505343 -3.715627338
## [512,] -4.50464866 0.984763042
## [513,] -3.16511083 1.024751078
## [514,] -2.32628002 1.003150308
## [515,] -5.66432496 -0.738047453
## [516,] 3.83260139 -3.395509344
## [517,] -7.07754986 -0.205574254
## [518,] -4.32165333 -3.878159894
## [519,] 0.06874606 2.141018777
## [520,] -2.53946269 -0.327800877
## [521,] -8.74098985 -0.678520256
## [522,] -4.90291101 -1.370442755
## [523,] -7.83094165 0.013577853
## [524,] -1.71140299 -2.361115761
## [525,] -6.09461319 4.263957331
## [526,] -7.63523619 0.243468931
## [527,] -6.52348791 -2.046223001
## [528,] -9.16619483 0.067058238
## [529,] -1.17057076 -1.269185745
## [530,] -6.91759237 1.448329723
## [531,] -2.88351644 -6.618586324
## [532,] -2.58513981 1.498316277
## [533,] -8.26577358 -2.659359416
## [534,] -1.96002915 -1.407240670
## [535,] 0.67888448 -4.195865867
## [536,] -2.82532288 1.992273823
## [537,] -2.72769884 -4.337732844
## [538,] -4.10567110 -2.713497273
## [539,] -4.93212530 -1.509708345
## [540,] -8.94530528 -2.237298836
## [541,] -1.11053515 -2.940303320
## [542,] -5.13150022 -2.571409917
## [543,] -2.51462476 -2.027585455
## [544,] -8.27143441 1.488077727
## [545,] -4.13249885 1.986547569
## [546,] -4.69657730 -2.096680508
## [547,] -7.57200412 -1.663253319
## [548,] -7.31080633 7.357177175
## [549,] -3.05449924 -6.293101307
## [550,] 0.58296004 4.389097329
## [551,] 4.32016528 -1.753106191
## [552,] -13.92654626 1.970300187
## [553,] -6.14292827 -2.806153481
## [554,] -6.19754951 -0.140599559
## [555,] -7.32970396 -2.516298391
## [556,] -9.62481209 -9.709668637
## [557,] -5.52590327 2.173694543
## [558,] -6.49396900 -0.993923148
## [559,] -4.03453689 -6.125297242
## [560,] -5.66191863 0.366700003
## [561,] -1.31218012 0.801344741
## [562,] 3.94018356 -0.629793892
## [563,] -3.11900500 -2.812039277
## [564,] -10.77915551 -4.109598289
## [565,] -7.02057021 0.543062134
## [566,] -0.83808033 0.431100493
## [567,] -4.23502750 -4.277048691
## [568,] 2.08037402 3.484804728
## [569,] -9.57189755 2.671659544
## [570,] -7.74317246 -0.642726204
## [571,] -7.77736606 2.111674301
## [572,] -3.39886792 1.572861582
## [573,] -4.82536290 -0.227986300
## [574,] -5.39689382 0.137073723
## [575,] -6.69064635 -2.048676733
## [576,] -6.28706421 -3.066062199
## [577,] -6.76240610 -2.521322543
## [578,] -5.35862698 -2.312823540
## [579,] -5.85290831 -2.692832098
## [580,] -6.00241669 -0.994031892
## [581,] -4.09635261 -1.264170995
## [582,] -5.62952268 -1.032235568
## [583,] -5.19960339 -1.606554971
## [584,] -2.62438370 -1.636357597
## [585,] -5.58434448 -1.746415549
## [586,] -4.93280507 -0.933709489
## [587,] -7.66350377 -2.309392149
## [588,] -5.99311430 0.559167341
## [589,] -5.17527039 -4.405893191
## [590,] -5.76078997 -0.975898307
## [591,] -6.52975206 -1.384358370
## [592,] -6.54788188 -2.728409671
## [593,] -4.33662236 -0.095719190
## [594,] -3.56523100 -5.992853861
## [595,] -5.18429454 -1.246342757
## [596,] -6.16260500 -1.310295041
## [597,] -3.81121728 -0.331137069
## [598,] -7.15754198 -2.273259599
## [599,] -5.81268634 -3.598745421
## [600,] -5.36248191 -2.850667028
## [601,] -5.64272974 -1.983990812
## [602,] -6.70969766 -1.317196967
## [603,] -5.35290286 -2.030124104
## [604,] -4.67106168 -2.950006729
## [605,] -5.78257815 -0.907473391
## [606,] -5.72258670 -2.717913222
## [607,] -4.65955099 -1.805624090
## [608,] -6.02858797 -2.637572044GGM.all.alive.6 = as.data.frame(GGMnetworkStats(P0.all.alive.6$sparseParCor, as.table = T))
GGM.all.dead.6 = as.data.frame(GGMnetworkStats(P0.all.dead.6$sparseParCor, as.table = T))
GGM.all.alive.6.order = GGM.all.alive.6[order(GGM.all.alive.6$degree, decreasing = T), ]
GGM.all.dead.6.order = GGM.all.dead.6[order(GGM.all.dead.6$degree, decreasing = T), ]
#Output top 5%
GGM.all.alive.6.order[1:round(nrow(GGM.all.alive.6.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos
## G6PD.RPPA 242 13947.5265 0.0011148272 0.9404069 130 112
## TFRC.RPPA 240 9488.6135 0.0011441648 1.0000000 113 127
## IGFBP2.RPPA 230 11586.2395 0.0011223345 0.9603509 124 106
## GATA3.RPPA 226 11771.5770 0.0010905125 0.8960354 117 109
## FASN.RPPA 218 9946.7011 0.0010752688 0.8813835 109 109
## PDCD4.RPPA 218 7204.6302 0.0011001100 0.9343471 109 109
## GAPDH.R2Gn 203 11345.2466 0.0010834236 0.8788641 109 94
## GAPDH.RPPA 202 11130.1275 0.0010775862 0.8068668 110 92
## MYH11.RPPA 196 5378.6923 0.0010672359 0.9013832 97 99
## EEF2.R2Gn 183 2813.6316 0.0010615711 0.8938545 94 89
## SQSTM1.R2Gn 178 3713.0405 0.0010626993 0.8611513 93 85
## ATM.RPPA 169 3209.7988 0.0010482180 0.8095259 87 82
## HSPA1A.R2Gn 168 3813.9873 0.0010256410 0.8012883 83 85
## TGM2.R2Gn 160 3676.8869 0.0010288066 0.7633667 78 82
## FN1.R2Gn 145 2699.6851 0.0009737098 0.6898722 70 75
## TTF1.RPPA 143 1785.4269 0.0009775171 0.7095083 72 71
## SYP.R2Gn 141 1084.7697 0.0009784736 0.7240031 67 74
## hsa-mir-122 135 1562.1181 0.0009910803 0.6866148 69 66
## IGFBP2.R2Gn 133 1373.5930 0.0009980040 0.6665976 73 60
## hsa-mir-137 132 1199.8894 0.0009950249 0.7181450 69 63
## hsa-mir-216b 132 1994.1774 0.0009784736 0.6567626 76 56
## hsa-mir-383 132 1365.0313 0.0009803922 0.6914947 74 58
## SERPINE1.R2Gn 130 1681.4925 0.0009765625 0.6496111 67 63
## MSH6.RPPA 128 1432.8787 0.0009680542 0.6591527 67 61
## RPS6.R2Gn 127 966.9534 0.0009990010 0.6650723 68 59
## hsa-mir-1305 122 736.4864 0.0009643202 0.6287396 60 62
## hsa-mir-296 122 1650.8241 0.0009569378 0.5623403 61 61
## PTEN.RPPA 122 1148.1097 0.0009842520 0.6647700 61 61
## hsa-mir-105-1 121 1097.9026 0.0009891197 0.6137457 58 63
## hsa-mir-206 121 645.5031 0.0009606148 0.6463505 63 58
## hsa-mir-873 119 1513.3086 0.0009871668 0.6161376 59 60
## EGFR.RPPA 118 1461.5681 0.0009920635 0.6433110 62 56
## hsa-mir-429 117 837.9607 0.0009756098 0.6033483 55 62
## hsa-mir-577 117 851.7913 0.0009832842 0.6252330 68 49
## hsa-mir-105-2 115 1060.3877 0.0009680542 0.5942184 61 54
## mutualInfo variance partialVar
## G6PD.RPPA 0.20232498 1.224246 1
## TFRC.RPPA 0.17774847 1.194525 1
## IGFBP2.RPPA 0.18418301 1.202236 1
## GATA3.RPPA 0.16180693 1.175633 1
## FASN.RPPA 0.13278951 1.142010 1
## PDCD4.RPPA 0.18740383 1.206114 1
## GAPDH.R2Gn 0.14473177 1.155730 1
## GAPDH.RPPA 0.20780696 1.230976 1
## MYH11.RPPA 0.16504122 1.179442 1
## EEF2.R2Gn 0.10732477 1.113296 1
## SQSTM1.R2Gn 0.11023139 1.116536 1
## ATM.RPPA 0.12303163 1.130920 1
## HSPA1A.R2Gn 0.08278371 1.086307 1
## TGM2.R2Gn 0.08313356 1.086687 1
## FN1.R2Gn 0.06071698 1.062598 1
## TTF1.RPPA 0.06633578 1.068585 1
## SYP.R2Gn 0.06053300 1.062403 1
## hsa-mir-122 0.05550907 1.057079 1
## IGFBP2.R2Gn 0.07692004 1.079956 1
## hsa-mir-137 0.05437659 1.055882 1
## hsa-mir-216b 0.05661494 1.058248 1
## hsa-mir-383 0.05681204 1.058457 1
## SERPINE1.R2Gn 0.05936896 1.061167 1
## MSH6.RPPA 0.04732089 1.048458 1
## RPS6.R2Gn 0.05362341 1.055087 1
## hsa-mir-1305 0.04591693 1.046987 1
## hsa-mir-296 0.04221873 1.043123 1
## PTEN.RPPA 0.06021776 1.062068 1
## hsa-mir-105-1 0.04328644 1.044237 1
## hsa-mir-206 0.05293718 1.054363 1
## hsa-mir-873 0.04839765 1.049588 1
## EGFR.RPPA 0.05215606 1.053540 1
## hsa-mir-429 0.04726128 1.048396 1
## hsa-mir-577 0.04077900 1.041622 1
## hsa-mir-105-2 0.04751180 1.048659 1GGM.all.dead.6.order[1:round(nrow(GGM.all.dead.6.order) * 0.05), ]## degree betweenness closeness eigenCentrality nNeg nPos
## FN1.R2Gn 311 22620.6721 0.0010917031 0.9997554 163 148
## TFRC.RPPA 303 16742.8315 0.0010976948 0.9904256 158 145
## MYH11.RPPA 299 15950.9398 0.0010893246 1.0000000 159 140
## GATA3.RPPA 286 16191.4659 0.0010775862 0.9517245 142 144
## HSPA1A.R2Gn 267 13878.9095 0.0010319917 0.9268846 137 130
## GAPDH.RPPA 264 14260.8928 0.0010482180 0.8735397 138 126
## GAPDH.R2Gn 236 7071.4516 0.0010141988 0.8521681 118 118
## EEF2.R2Gn 234 6513.1973 0.0010193680 0.8830306 107 127
## SYP.R2Gn 209 5317.4972 0.0009861933 0.8328442 100 109
## G6PD.RPPA 203 4259.5733 0.0009746589 0.7947036 110 93
## FASN.RPPA 203 4718.7695 0.0009756098 0.7745888 99 104
## SQSTM1.R2Gn 202 6861.2948 0.0009881423 0.7709728 106 96
## RPS6.R2Gn 193 4282.2118 0.0009746589 0.7720015 101 92
## ATM.RPPA 192 3299.9567 0.0009442871 0.7600591 99 93
## CTNNB1.R2Gn 185 4151.2341 0.0009699321 0.7475606 87 98
## PDCD4.RPPA 182 2796.2805 0.0009689922 0.7805713 92 90
## hsa-mir-499 169 4440.7788 0.0009216590 0.6513608 86 83
## hsa-mir-122 159 1150.3890 0.0009469697 0.7176233 84 75
## TUBA1B.R2Gn 158 2146.6335 0.0009208103 0.6783887 79 79
## ADAR.R2Gn 156 1289.9590 0.0009132420 0.6710976 82 74
## MSH6.RPPA 148 1076.4023 0.0009233610 0.6446882 71 77
## CCND1.R2Gn 146 1986.9998 0.0009293680 0.6198953 74 72
## hsa-mir-137 142 1401.8295 0.0009216590 0.6172048 79 63
## hsa-mir-135a-2 141 1169.9459 0.0009285051 0.6487865 71 70
## hsa-mir-124-3 139 845.2028 0.0009242144 0.6550325 78 61
## hsa-mir-34b 139 948.4423 0.0009149131 0.6313927 67 72
## hsa-mir-3662 139 1004.1023 0.0009174312 0.6439292 73 66
## hsa-mir-124-2 134 1196.4753 0.0009115770 0.6272339 72 62
## RBM15.RPPA 133 1065.1950 0.0008849558 0.5427402 68 65
## hsa-mir-200a 129 838.5248 0.0008756567 0.5605142 68 61
## hsa-mir-216a 129 784.9285 0.0009009009 0.5849457 67 62
## PEA15.RPPA 126 631.1033 0.0009124088 0.5857375 69 57
## PTEN.RPPA 124 757.7862 0.0008880995 0.5712753 70 54
## hsa-mir-1269 123 1025.5123 0.0009074410 0.6004190 55 68
## hsa-mir-3166 123 724.7278 0.0008779631 0.5609606 56 67
## mutualInfo variance partialVar
## FN1.R2Gn 0.19503360 1.215352 1
## TFRC.RPPA 0.15880524 1.172110 1
## MYH11.RPPA 0.15679643 1.169757 1
## GATA3.RPPA 0.14802433 1.159541 1
## HSPA1A.R2Gn 0.14206675 1.152654 1
## GAPDH.RPPA 0.12355493 1.131512 1
## GAPDH.R2Gn 0.10163009 1.106974 1
## EEF2.R2Gn 0.08044111 1.083765 1
## SYP.R2Gn 0.06894869 1.071381 1
## G6PD.RPPA 0.04879533 1.050005 1
## FASN.RPPA 0.06862232 1.071032 1
## SQSTM1.R2Gn 0.06649237 1.068753 1
## RPS6.R2Gn 0.06600052 1.068227 1
## ATM.RPPA 0.05388770 1.055366 1
## CTNNB1.R2Gn 0.05815197 1.059876 1
## PDCD4.RPPA 0.06301900 1.065047 1
## hsa-mir-499 0.04586515 1.046933 1
## hsa-mir-122 0.03357344 1.034143 1
## TUBA1B.R2Gn 0.02909487 1.029522 1
## ADAR.R2Gn 0.04145008 1.042321 1
## MSH6.RPPA 0.04707180 1.048197 1
## CCND1.R2Gn 0.04373390 1.044704 1
## hsa-mir-137 0.02649739 1.026852 1
## hsa-mir-135a-2 0.03450445 1.035107 1
## hsa-mir-124-3 0.03233885 1.032867 1
## hsa-mir-34b 0.03504260 1.035664 1
## hsa-mir-3662 0.03207855 1.032599 1
## hsa-mir-124-2 0.02809583 1.028494 1
## RBM15.RPPA 0.02392128 1.024210 1
## hsa-mir-200a 0.02864010 1.029054 1
## hsa-mir-216a 0.02667264 1.027032 1
## PEA15.RPPA 0.02553695 1.025866 1
## PTEN.RPPA 0.02205928 1.022304 1
## hsa-mir-1269 0.02359496 1.023876 1
## hsa-mir-3166 0.02476510 1.025074 1Colors.6.plot.a <- rownames(GGM.all.alive.6.order)
Colors.6.plot.a[grep("hsa", rownames(GGM.all.alive.6.order))] <- "miRNASeqGene"
Colors.6.plot.a[grep(".RPPA", rownames(GGM.all.alive.6.order))] <- "RPPA Array"
Colors.6.plot.a[grep(".R2Gn", rownames(GGM.all.alive.6.order))] <- "RNASeq2GeneNorm"ggplot(GGM.all.alive.6.order, aes(x = reorder(rownames(GGM.all.alive.6.order), -degree), y = degree, color = Colors.6.plot.a)) +
geom_point() +
geom_hline(yintercept = mean(GGM.all.alive.6.order$degree), linetype = "dashed", color = "red") +
# 36th unit: top 5%
geom_hline(yintercept = GGM.all.alive.6.order[36,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "All Data", guide = guide_axis(angle = 90)) +
scale_color_manual(values = c("red", "blue", "green3"))+
ggtitle("Variables sorted by degree, FDR = 1-1e-6, Surviving Patients")
Colors.6.plot.d <- rownames(GGM.all.dead.6.order)
Colors.6.plot.d[grep("hsa", rownames(GGM.all.dead.6.order))] <- "miRNASeqGene"
Colors.6.plot.d[grep(".RPPA", rownames(GGM.all.dead.6.order))] <- "RPPA Array"
Colors.6.plot.d[grep(".R2Gn", rownames(GGM.all.dead.6.order))] <- "RNASeq2GeneNorm"ggplot(GGM.all.dead.6.order, aes(x = reorder(rownames(GGM.all.dead.6.order), -degree), y = degree, color = Colors.6.plot.d)) +
geom_point() +
geom_hline(yintercept = mean(GGM.all.dead.6.order$degree), linetype = "dashed", color = "red") +
# 36th unit: top 5%
geom_hline(yintercept = GGM.all.dead.6.order[36,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "All Data", guide = guide_axis(angle = 90)) +
scale_color_manual(values = c("red", "blue", "green3"))+
ggtitle("Variables sorted by degree, FDR = 1-1e-6, Deceased Patients")
Save Gaussian Graphical Model Newotk Statistics to file
The variables are order by degree (number of edges connected to variable node)
out.RPPA = list("FDR1e-1" = GGM.RPPA.order)
out.R2Gn = list("FDR1e-6" = GGM.R2Gn.6.order,
"FDR1e-14" = GGM.R2Gn.min.order)
out.mRSG = list("FDR1e-6" = GGM.mRSG.6.order,
"FDR1e-13" = GGM.mRSG.min.order)
out.all = list("FDR1e-6" = GGM.all.6.order,
"FDR1e-14" = GGM.all.min.order)
out.RPPA.alive = list("FDR1e-1" = GGM.RPPA.alive.order)
out.RPPA.dead = list("FDR1e-1" = GGM.RPPA.dead.order)
out.R2Gn.alive = list("FDR1e-6" = GGM.R2Gn.alive.6.order,
"FDR1e-14" = GGM.R2Gn.alive.min.order)
out.R2Gn.dead = list("FDR1e-6" = GGM.R2Gn.dead.6.order,
"FDR1e-14" = GGM.R2Gn.dead.min.order)
#out.PR.alive = list("FDR1e-6" = GGM.PR.alive.6.order,
# "FDR1e-14" = GGM.PR.alive.min.order)
#out.PR.dead = list("FDR1e-6" = GGM.PR.dead.6.order,
# "FDR1e-14" = GGM.PR.dead.min.order)
out.mRSG.alive = list("FDR1e-6" = GGM.mRSG.alive.6.order,
"FDR1e-13" = GGM.mRSG.alive.min.order)
out.mRSG.dead = list("FDR1e-6" = GGM.mRSG.dead.6.order,
"FDR1e-13" = GGM.mRSG.dead.min.order)
out.all.alive = list("FDR1e-6" = GGM.all.alive.6.order,
"FDR1e-14" = GGM.all.alive.min.order)
out.all.dead = list("FDR1e-6" = GGM.all.dead.6.order,
"FDR1e-14" = GGM.all.dead.min.order)
out.RPPA.ad = list("alive" = out.RPPA.alive,
"dead" = out.RPPA.dead)
out.R2Gn.ad = list("alive" = out.R2Gn.alive,
"dead" = out.R2Gn.dead)
#out.PR.ad = list("alive" = out.PR.alive,
# "dead" = out.PR.dead)
out.mRSG.ad = list("alive" = out.mRSG.alive,
"dead" = out.mRSG.dead)
out.all.ad = list("alive" = out.all.alive,
"dead" = out.all.dead)
out.fullsample = list("RPPA Array" = out.RPPA,
"RNASeq2GeneNorm" = out.R2Gn,
"miRNASeqGene" = out.mRSG,
"all" = out.all)
out.splitbyad = list("RPPA Array" = out.RPPA.ad,
"RNASeq2GeneNorm" = out.R2Gn.ad,
#"RPPA + R2Gn" = out.PR.ad)
"miRNASeqGene" = out.mRSG.ad,
"all" = out.all.ad)
out.ggm = list("full_sample" = out.fullsample,
"by_vital_status" = out.splitbyad)
saveRDS(out.ggm, file = "data/sel_features/results_ggm_network_stats_all.rds")The code below are not executed.
The below was a replacement for the “all dataset search” as optimal lambda was once considered not found for deceased patients , for miRNASeqGene and all data. They are no longer needed when lambda can be found with lambdaMax set to 10 instead of 1000.
F. RPPA Array + RNASeq2GeneNorm
data.PR = data.numeric[, c(group.RPPA, group.R2Gn)]data.PR.alive = data.PR[which(data.Y == 0), ]
data.PR.dead = data.PR[which(data.Y == 1), ]set.seed(42)
opt.PR.alive = optPenalty.kCVauto(Y = data.PR.alive, lambdaMin = 1e-11, lambdaMax = 10)
opt.PR.dead = optPenalty.kCVauto(Y = data.PR.dead, lambdaMin = 1e-11, lambdaMax = 10)
opt.PR.alive$optLambda
opt.PR.dead$optLambdaedgeHeat(opt.PR.alive$optPrec, diag = F, textsize = 1)
edgeHeat(opt.PR.dead$optPrec, diag = F, textsize = 1)Smallest possible FDRcut:
P0.PR.alive.min = sparsify(opt.PR.alive$optPrec, threshold = "localFDR", FDRcut=1-1e-14)
P0.PR.dead.min = sparsify(opt.PR.dead$optPrec, threshold = "localFDR", FDRcut=1-1e-14)Colors for the different omics:
PcorP.PR.a.min = pruneMatrix(P0.PR.alive.min$sparseParCor)
Colors.PR.a.min = rownames(PcorP.PR.a.min)
Colors.PR.a.min[grep(".RPPA", rownames(PcorP.PR.a.min))] <- "green"
Colors.PR.a.min[grep(".R2Gn", rownames(PcorP.PR.a.min))] <- "cyan"PcorP.PR.d.min = pruneMatrix(P0.PR.dead.min$sparseParCor)
Colors.PR.d.min = rownames(PcorP.PR.d.min)
Colors.PR.d.min[grep(".RPPA", rownames(PcorP.PR.d.min))] <- "green"
Colors.PR.d.min[grep(".R2Gn", rownames(PcorP.PR.d.min))] <- "cyan"#fig.width=20, fig.height=20}
#dev.new(width = 20, height = 20, unit = "in", noRstudioGD = F)
set.seed(42)
Ugraph(PcorP.PR.a.min, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
Vcolor = Colors.PR.a.min,
main = "RPPA Array + RNASeq2GeneNorm data (Surviving Patients)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\n Blue: RNASeq2Gene; Green:RPPA Array")
Ugraph(PcorP.PR.d.min, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
Vcolor = Colors.PR.d.min,
main = "RPPA Array + RNASeq2GeneNorm data (Deceased Patients)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\n Blue: RNASeq2Gene; Green:RPPA Array")
#Ugraph(P0.PR.alive.min$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
# Vcolor = Colors.PR,
# main = "RPPA Array + RNASeq2GeneNorm data (Surviving Patiens)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\\n Blue: RNASeq2Gene; Green:RPPA Array")
#Ugraph(P0.PR.dead.min$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
# Vcolor = Colors.PR,
# main = "RPPA Array + RNASeq2GeneNorm data (Deceased Patiens)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\n Blue: RNASeq2Gene; Green:RPPA Array")GGM.PR.alive.min = as.data.frame(GGMnetworkStats(P0.PR.alive.min$sparseParCor, as.table = T))
GGM.PR.dead.min = as.data.frame(GGMnetworkStats(P0.PR.dead.min$sparseParCor, as.table = T))
GGM.PR.alive.min.order = GGM.PR.alive.min[order(GGM.PR.alive.min$degree, decreasing = T), ]
GGM.PR.dead.min.order = GGM.PR.dead.min[order(GGM.PR.dead.min$degree, decreasing = T), ]
#Output top 5%
GGM.PR.alive.min.order[1:round(nrow(GGM.PR.alive.min.order) * 0.05), ]
GGM.PR.dead.min.order[1:round(nrow(GGM.PR.dead.min.order) * 0.05), ]Colors.min.plot.a <- rownames(GGM.PR.alive.min.order)
#Colors.min.plot.a[grep("hsa", rownames(GGM.PR.alive.min.order))] <- "red"
Colors.min.plot.a[grep(".RPPA", rownames(GGM.PR.alive.min.order))] <- "RPPA Array"
Colors.min.plot.a[grep(".R2Gn", rownames(GGM.PR.alive.min.order))] <- "RNASeq2GeneNorm"#fig.width=24, fig.height=8}
ggplot(GGM.PR.alive.min.order, aes(x = reorder(rownames(GGM.PR.alive.min.order), -degree), y = degree, color = Colors.min.plot.a)) +
geom_point() +
geom_hline(yintercept = mean(GGM.PR.alive.min.order$degree), linetype = "dashed", color = "red") +
# 12th unit: top 5%
geom_hline(yintercept = GGM.PR.alive.min.order[12,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "RPPA Array + RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
scale_color_manual(values = c("blue", "green3"))+
ggtitle("Variables sorted by degree, FDR = 1-1e-14, Surviving Patients")Colors.min.plot.d <- rownames(GGM.PR.dead.min.order)
#Colors.min.plot.d[grep("hsa", rownames(GGM.PR.dead.min.order))] <- "red"
Colors.min.plot.d[grep(".RPPA", rownames(GGM.PR.dead.min.order))] <- "RPPA Array"
Colors.min.plot.d[grep(".R2Gn", rownames(GGM.PR.dead.min.order))] <- "RNASeq2GeneNorm"#fig.width=24, fig.height=8}
ggplot(GGM.PR.dead.min.order, aes(x = reorder(rownames(GGM.PR.dead.min.order), -degree), y = degree, color = Colors.min.plot.d)) +
geom_point() +
geom_hline(yintercept = mean(GGM.PR.dead.min.order$degree), linetype = "dashed", color = "red") +
# 12th unit: top 5%
geom_hline(yintercept = GGM.PR.dead.min.order[12,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "RPPA Array + RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
scale_color_manual(values = c("blue", "green3"))+
ggtitle("Variables sorted by degree, FDR = 1-1e-14, Deceased Patients")FDRcut 1e-6:
P0.PR.alive.6 = sparsify(opt.PR.alive$optPrec, threshold = "localFDR", FDRcut=1-1e-6)
P0.PR.dead.6 = sparsify(opt.PR.dead$optPrec, threshold = "localFDR", FDRcut=1-1e-6)PcorP.PR.a.6 = pruneMatrix(P0.PR.alive.6$sparseParCor)
Colors.PR.a.6 = rownames(PcorP.PR.a.6)
Colors.PR.a.6[grep(".RPPA", rownames(PcorP.PR.a.6))] <- "green"
Colors.PR.a.6[grep(".R2Gn", rownames(PcorP.PR.a.6))] <- "cyan"PcorP.PR.d.6 = pruneMatrix(P0.PR.dead.6$sparseParCor)
Colors.PR.d.6 = rownames(PcorP.PR.d.6)
Colors.PR.d.6[grep(".RPPA", rownames(PcorP.PR.d.6))] <- "green"
Colors.PR.d.6[grep(".R2Gn", rownames(PcorP.PR.d.6))] <- "cyan"# fig.width=20, fig.height=20}
#dev.new(width = 20, height = 20, unit = "in", noRstudioGD = F)
set.seed(42)
Ugraph(PcorP.PR.a.6, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
Vcolor = Colors.PR.a.6,
main = "RPPA Array + RNASeq2GeneNorm data (Surviving Patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\n Blue: RNASeq2Gene; Green:RPPA Array")
Ugraph(PcorP.PR.d.6, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
Vcolor = Colors.PR.d.6,
main = "RPPA Array + RNASeq2GeneNorm data (Deceased Patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\n Blue: RNASeq2Gene; Green:RPPA Array")
#Ugraph(P0.PR.alive.6$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
# Vcolor = Colors.PR,
# main = "RPPA Array + RNASeq2GeneNorm data (Surviving Patiens)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\\n Blue: RNASeq2Gene; Green:RPPA Array")
#Ugraph(P0.PR.dead.6$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
# Vcolor = Colors.PR,
# main = "RPPA Array + RNASeq2GeneNorm data (Deceased Patiens)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\n Blue: RNASeq2Gene; Green:RPPA Array")GGM.PR.alive.6 = as.data.frame(GGMnetworkStats(P0.PR.alive.6$sparseParCor, as.table = T))
GGM.PR.dead.6 = as.data.frame(GGMnetworkStats(P0.PR.dead.6$sparseParCor, as.table = T))
GGM.PR.alive.6.order = GGM.PR.alive.6[order(GGM.PR.alive.6$degree, decreasing = T), ]
GGM.PR.dead.6.order = GGM.PR.dead.6[order(GGM.PR.dead.6$degree, decreasing = T), ]
#Output top 5%
GGM.PR.alive.6.order[1:round(nrow(GGM.PR.alive.6.order) * 0.05), ]
GGM.PR.dead.6.order[1:round(nrow(GGM.PR.dead.6.order) * 0.05), ]Colors.6.plot.a <- rownames(GGM.PR.alive.6.order)
#Colors.6.plot.a[grep("hsa", rownames(GGM.PR.alive.6.order))] <- "red"
Colors.6.plot.a[grep(".RPPA", rownames(GGM.PR.alive.6.order))] <- "RPPA Array"
Colors.6.plot.a[grep(".R2Gn", rownames(GGM.PR.alive.6.order))] <- "RNASeq2GeneNorm"#fig.width=24, fig.height=8}
ggplot(GGM.PR.alive.6.order, aes(x = reorder(rownames(GGM.PR.alive.6.order), -degree), y = degree, color = Colors.6.plot.a)) +
geom_point() +
geom_hline(yintercept = mean(GGM.PR.alive.6.order$degree), linetype = "dashed", color = "red") +
# 12th unit: top 5%
geom_hline(yintercept = GGM.PR.alive.6.order[12,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "RPPA Array + RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
scale_color_manual(values = c("blue", "green3"))+
ggtitle("Variables sorted by degree, FDR = 1-1e-6, Surviving Patients")Colors.6.plot.d <- rownames(GGM.PR.dead.6.order)
#Colors.6.plot.d[grep("hsa", rownames(GGM.PR.dead.6.order))] <- "red"
Colors.6.plot.d[grep(".RPPA", rownames(GGM.PR.dead.6.order))] <- "RPPA Array"
Colors.6.plot.d[grep(".R2Gn", rownames(GGM.PR.dead.6.order))] <- "RNASeq2GeneNorm"#fig.width=24, fig.height=8}
ggplot(GGM.PR.dead.6.order, aes(x = reorder(rownames(GGM.PR.dead.6.order), -degree), y = degree, color = Colors.6.plot.d)) +
geom_point() +
geom_hline(yintercept = mean(GGM.PR.dead.6.order$degree), linetype = "dashed", color = "red") +
# 12th unit: top 5%
geom_hline(yintercept = GGM.PR.dead.6.order[12,]$degree, linetype = "dashed", color = "darkgreen") +
scale_x_discrete(name = "RPPA Array + RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
scale_color_manual(values = c("blue", "green3"))+
ggtitle("Variables sorted by degree, FDR = 1-1e-6, Deceased Patients")